Aquatic plant surveys and evaluation of aquatic plant harvesting in Arizona reservoirs

              Technical Guidance Bulletin No. 9 – November 2007
Aquatic Plant Surveys and Evaluation of Aquatic Plant
Harvesting in Arizona Reservoirs
Federal Aid in Sport Fish Restoration
Project F-14-R
Anthony T. Robinson, James E. Fulmer,
Lorraine D. Avenetti
Research Branch
Arizona Game and Fish Department
5000 W. Carefree Highway
Phoenix, Arizona 85086
Cover photographs: Top--Pena Blanca Lake by Anthony Robinson; Bottom left—aquatic
weed harvester on Sunrise Reservoir, unknown photographer; bottom right—aquatic
weed harvester on Rainbow Lake, unknown photographer.
Aquatic Plant Surveys and
Evaluation of Aquatic Plant Harvesting in Arizona Reservoirs
Anthony T. Robinson
James E. Fulmer
Lorraine D. Avenetti
Research Branch
Arizona Game and Fish Department
5000 W. Carefree Highway
Phoenix, Arizona 85086
Arizona Game and Fish Department Mission
To conserve, enhance, and restore Arizona’s diverse wildlife resources and habitats
through aggressive protection and management programs, and to provide wildlife
resources and safe watercraft and off-highway vehicle recreation for the enjoyment,
appreciation, and use by present and future generations.
The Arizona Game and Fish Department prohibits discrimination on the basis of race,
color, sex, national origin, age, or disability in its programs and activities. If anyone
believes they have been discriminated against in any of AGFD’s programs or activities,
including its employment practices, the individual may file a complaint alleging
discrimination directly with AGFD Deputy Director, 5000 W. Carefree Highway,
Phoenix, AZ 85086, (623) 236-3290 or U.S. Fish and Wildlife Service, 4040 N. Fairfax
Dr., Ste. 130, Arlington, VA 22203.
Persons with a disability may request a reasonable accommodation, such as a sign
language interpreter, or this document in an alternative format, by contacting the AGFD
Deputy Director, 5000 W. Carefree Highway, Phoenix, AZ 85086, (623) 236-3290, or by
calling TTY at 1-800-367-8939. Requests should be made as early as possible to allow
sufficient time to arrange for accommodation.
Suggested Citation:
Robinson, A. T., J. E. Fulmer, and L. D. Avenetti. 2007. Aquatic plant surveys
and evaluation of aquatic plant harvesting in Arizona reservoirs. Arizona Game
and Fish Department, Research Branch, Technical Guidance Bulletin No. 9,
Phoenix. 39 pp.
Federal Aid in Sport Fish
Restoration Project F-14-R
Funded by your purchases of
fishing equipment
ACKNOWLEDGEMENTS
The Sport Fish Restoration State Trust Grant F-14-R program funded this project. A special
thanks to all who helped with the fieldwork: Richard Dryer, Dannette Ihle, John Millican, Mike
Sumner, Bill Stewart, David Williams, and to Sue Boe for the maps of aquatic vegetation
coverage. We appreciate all the helpful comments on the draft report provided by Chantal
O’Brien, Bill Persons, and David Ward.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
iv
TABLE OF CONTENTS
ACKNOWLEDGEMENTS .................................................................................................. iii
TABLE OF CONTENTS ...................................................................................................... iv
EXECUTIVE SUMMARY .....................................................................................................v
INTRODUCTION...................................................................................................................1
METHODS..............................................................................................................................3
Statewide Aquatic Plant Survey.............................................................................................3
Study Sites .......................................................................................................................... 3
Methods .............................................................................................................................. 3
Evaluation of Harvesting Program.........................................................................................5
Study Sites .......................................................................................................................... 5
Aquatic Vegetation Coverage............................................................................................. 5
Fish Kills............................................................................................................................ 6
Water Chemistry ................................................................................................................. 6
Operational Cost of Harvesting .......................................................................................... 7
Incidental Fish Collection................................................................................................... 8
Angler Use Survey.............................................................................................................. 8
RESULTS ................................................................................................................................9
Statewide Aquatic Plant Survey.............................................................................................9
Evaluation of Harvesting Program.......................................................................................14
Aquatic Vegetation Coverage........................................................................................... 14
Fish Kills.......................................................................................................................... 15
Water Chemistry ............................................................................................................... 15
Operational Cost of Harvesting ........................................................................................ 21
Incidental Fish Collection................................................................................................. 21
Angler Use Survey............................................................................................................ 22
DISCUSSION........................................................................................................................25
Statewide Aquatic Plant Survey...........................................................................................25
Evaluation of Harvesting Program.......................................................................................27
Aquatic Vegetation Coverage, Fish Kills, and Water Chemistry ..................................... 27
Operational Cost of Harvesting ........................................................................................ 28
Incidental Fish................................................................................................................... 29
Angler Survey ................................................................................................................... 29
MANAGEMENT OPTIONS ................................................................................................29
Cost and Benefits of Harvesting ..........................................................................................29
Costs: ............................................................................................................................... 29
Benefits: ............................................................................................................................ 30
LITERATURE CITED .........................................................................................................30
APPENDIX............................................................................................................................33
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
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EXECUTIVE SUMMARY
The goal of our study was to develop
information to help manage aquatic plants in
Arizona’s reservoirs to benefit sport fish
management activities and angler access.
To attain this goal we surveyed aquatic
plants in reservoirs throughout Arizona and
evaluated if the Arizona Game and Fish
Department’s aquatic weed harvesting
program was benefiting the fisheries
program.
We surveyed aquatic plants in 38 reservoirs
throughout Arizona from 2004 through 2006
to develop an inventory of species, and to
determine species distribution and
composition patterns. Two non-native
aquatic plant species were found during the
surveys: Eurasian watermilfoil
(Myriophyllum spicatum) was found in nine
reservoirs and curly-leafed pondweed
(Potamogeton crispus) was found at two
reservoirs. Among reservoirs, the most
prevalent aquatic plants were filamentous
algae and another algae, muskgrass (Chara
spp.), and the vascular plants cattails (Typha
spp.) and hard-stem bulrush (Schoenoplectus
acutus) followed by coontail
(Ceratophyllum demersum), sago pondweed
(Stuckenia pectinatus), and northern
watermilfoil (Myriophyllum sibiricum).
Within reservoirs, coontail or sago
pondweed dominated the plant community
at five reservoirs, northern watermilfoil at
eight reservoirs and Eurasian watermilfoil at
four reservoirs. Elevation and depth were
significant predictors of occurrence for
several species, and the number of aquatic
plant taxa was positively related to reservoir
surface area. Seven taxa, including
filamentous algae, Eurasian watermilfoil,
curly-leafed pondweed, coontail, sago
pondweed, spiny naiad (Najas marina), and
northern watermilfoil, are probably the best
targets for management because they had
high prevalence and percent composition in
Arizona, and hence are most likely to be
considered problematic.
To evaluate if the Arizona Game and Fish
Department’s aquatic weed harvesting
program was benefiting the fisheries
program, we examined whether angler
access and water chemistry differed before
to after harvesting at four reservoirs during
2005 and four reservoirs during 2006. We
also examined the financial cost of
harvesting, the amount of fish incidentally
removed during the harvesting process, and
surveyed anglers at nine reservoirs to
determine their attitudes towards aquatic
weeds and aquatic weed control. The
benefits of aquatic plant harvesting were that
harvesting did result in immediate reduction
in aquatic plant coverage (i.e., improved
access) at most reservoirs monitored, and
anglers were overwhelmingly (82%) in
favor of controlling aquatic vegetation.
Plus, the financial cost of the harvesting
program is relatively small ($50,600/year)
compared to other states where millions of
dollars are spent. However, aquatic weed
harvesting did not appear to have the
beneficial effects on water chemistry that we
expected; we did not detect decreased pH or
nutrient concentrations or increased
dissolved oxygen concentrations subsequent
to harvesting. Aquatic weed harvesting did
remove some fish, most of which were game
fish, but most, if not all, were expendable
young-of-year fish. Another, potentially
more serious cost, was that the aquatic plant
harvesting program has likely resulted in the
spread of the invasive Eurasian watermilfoil
to reservoirs throughout Arizona. Aquatic
plant harvesting is probably a worthwhile
endeavor to improve angler access and keep
our angling customers satisfied. However,
we strongly recommend that more effective
decontamination procedures be implemented
to limit the spread of invasive species.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
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INTRODUCTION
Aquatic freshwater plants tend to have large-scale
distributions (Santamaria 2002), and at
a local scale, play an important role in
aquatic ecosystems. However, excessive
aquatic plant densities and biomass can be
considered problematic. Lembi (2003)
summarized problems associated with
excessive aquatic plant density as follows.
Recreational activities such as swimming,
fishing, and boating can be impaired or
prevented. Excessive densities and biomass
can also result in stunted fish growth and
overpopulation of small-bodied fishes. This
occurs because the production of too much
vegetative cover prevents effective predation
of small fish by larger fish. Excessive
aquatic plant growths can also decrease
localized dissolved oxygen levels, which can
cause fish kills. Oxygen levels are affected
by the diel cycle of photosynthesis (oxygen
levels are high during the day) and
respiration (night-time oxygen levels are
depleted). If plant biomass is excessive,
nighttime respiration by aquatic plants can
consume most of the dissolved oxygen in
the water within the macrophyte beds to
levels less than 1-2 mg/L. Furthermore,
excessive growth during the summer results
in large quantities of organic matter, that
when decomposed via bacteria and
microbes, results in high rates of microbial
respiration and thus oxygen consumption.
Similar processes can occur in the winter for
lakes that freeze. Snow cover over ice
decreases light levels and reduces or
prevents photosynthesis and oxygen
production, but organic matter continues to
be decomposed by bacteria, thus consuming
oxygen. Other problems associated with
excessive plant growth include: 1) aquatic
plants provide stagnant habitat ideal for
mosquito breeding, 2) certain algae can
impart foul tastes and odors to the water,
and can produce substances toxic to fish and
wildlife, 3) plants impede water flow in
ditches, canals, and culverts and cause water
to back up, 4) deposition of dead organic
matter can cause the gradual filling in of
water bodies, 5) nutrients, particularly
organic carbon and phosphorus, released
from senescent plants into the water can
result in algal blooms, 6) excessive growth
can lower property values and decrease
aesthetic appeal, and 7) invasion of
nonnative plants (i.e., invasive species) can
cause shifts in community structure and
function that may negatively impact native
animal and plant species. Aquatic plants are
often managed to alleviate some or all of the
above mentioned problems.
Arizona Game and Fish Department
(Department) has used several techniques to
manage aquatic plants in Arizona’s sport-fishing
reservoirs since the 1980s to help
manage fisheries and improve angler access.
Triploid grass carp (Ctenopharyngodon
idella) are used to control aquatic plants in
some isolated reservoirs, and in canals and
some golf course ponds. Prior to 1980, the
Department primarily used herbicides
(diquat) to manage aquatic nuisance plants
(i.e., aquatic weeds) on public reservoirs and
ponds, and herbicides are still used in urban
waters. However, the public objected to the
use of herbicides in non-urban reservoirs
(specifically Arivaca Lake) during the early
1980s, and other control measures were
investigated. From 1982 through 1990 the
Department used an Aquamarine H-650
Harvester, which both cut and harvested
weeds. In 1985, Department acquired a
Hockney HC-10 Aquatic Weed Cutter by a
donation from Northern Arizona Flycasters
to control aquatic weeds in a few shallow
reservoirs; this piece of machinery cut the
vegetation, which then had to be removed
(harvested) with an attached rake or raked
by hand. In 1990, the Department
purchased an Aquarius Systems H-620
Aquatic Plant Harvester (which both cut and
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
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harvested weeds), to replace the H-650. A
second, smaller harvester (Aquarius Systems
HM-220 Aquatic Plant Harvester) was
purchased in 1995. Harvesting was the
primary means the Department used to
control aquatic plants in non-urban
reservoirs and ponds from 1982 to present.
The purported benefits of aquatic plant
harvesting in Arizona include: (a) improved
angler access, (b) a decrease in pH which
can then allow for extended periods of trout
stocking during the summer, (c) greater
dissolved oxygen concentrations which
decrease the chance of summer kills, and (d)
a decrease in nutrients which will lessen
algal blooms. With respect to the latter
three benefits, macrophytes are reported
(Wetzel 1983, Carpenter and Lodge 1987,
Carter et al. 1991) to affect pH and
dissolved oxygen concentrations within the
macrophyte beds, and when they senesce,
result in increased nutrient levels.
The Department’s regional fisheries
program managers determine which
reservoirs they would like harvested, and the
Development Branch is responsible for
harvesting aquatic plants from those
reservoirs. Aquatic weeds have been
harvested from 27 reservoirs and ponds
since the program began. In a typical year,
harvesting is done May through October,
and six reservoirs (average number
harvested between 1997 and 2006) are
harvested, one or two of which are usually
harvested twice in one year. On average
during the 1997-2006 period, three
reservoirs per year were harvested using the
H-620 (approximately 3 weeks per
reservoir) and three reservoirs per year were
harvested with the HM-220 harvester
(approximately 1-2 weeks per reservoir).
The H-650 was and the H-620 is used on
larger and deeper reservoirs because of their
greater draft, whereas the HM-220 and HC-
10, because their drafts are less, are used on
shallower reservoirs. Specifications for the
harvesters and cutter are given in Table 1.
The three harvesters can only be used on
reservoirs that have a boat ramp of sufficient
depth to allow launching. The Hockney
HC-10 is a relatively small watercraft and
can be launched on most reservoirs with a
boat ramp. When the H-620 or H-650 is
used, the strategy is to harvest the bulk of
the vegetation in the center of the lake and
then harvest the shorelines. For the HM-220
and HC-10, the strategy is to harvest as
much as possible for small reservoirs, but
for larger reservoirs, only boating lanes or
areas around docks or near-shore recreation
areas are targeted. The plant material
harvested is transferred to a dump truck and
taken to an approved dump site.
The goal of our study was to develop
information to help manage aquatic plants in
Arizona’s reservoirs to benefit sport fish
management activities and angler access.
Our first objective was to develop an
inventory of aquatic plant species found in
sport-fishing reservoirs throughout Arizona
in order to determine species distribution
and composition patterns. These data will
help focus management actions on
problematic aquatic plant species. The
second objective of this study was to
evaluate if the Department’s aquatic weed
harvesting program was benefiting the
Table 1. Specifications of aquatic plant harvesters
used by Arizona Game and Fish Department.
Capacity
Harvester
Model
Max.
cut
depth
(m)
Cutting
width
(m) (m3) (kg)
Aquamarine
H-650 1.52 2.44 18.4 4,536
Aquarius
Systems H-620 1.68 2.74 23.5 5,371
Aquarius
Systems HM-220 1.68 1.52 7.4 2,948
Hockney HC-10 1.5 3.0 --- ---
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3
fisheries program. To address the second
objective, we examined whether angler
attitudes, angler access, and water chemistry
differed before and after harvesting at
selected reservoirs. We also examined the
financial cost of harvesting, as well as the
amount of fish incidentally removed during
the harvesting process.
METHODS
STATEWIDE AQUATIC PLANT
SURVEY
Study Sites
Our goal was to survey aquatic plants in a
minimum of one sport-fish reservoir from
each of the U.S. Geological Survey
watersheds in Arizona (8-digit Hydrologic
Unit Code: HUC). Forty-eight of the 84
HUCs in Arizona have a reservoir or pond
with sport fish present. We excluded HUCs
on tribal lands, except for the Navajo and
Hopi Nations where we were permitted
access, resulting in 45 potential HUCs to
survey. Reservoirs were targeted for the
presence of sport fish and a boat ramp, but if
such water bodies were not found within a
HUC, water bodies without boat ramps were
considered. Water bodies were randomly
selected from each HUC for sampling.
However, we wanted to survey all reservoirs
where Arizona Game and Fish Department
had harvested aquatic vegetation in the past,
so in some instances, more than one water
body per HUC were surveyed. Surveys
were conducted June through October
during the period when aquatic macrophytes
are flowering to allow for easier
identification.
Methods
Aquatic macrophytes were surveyed using
two point-transect methods similar to the
line intercept method described in Titus
(1993). In reservoirs less than or equal to
five meters in depth, we determined the
length of the long axis by measuring it on a
topographic map (TOPO! 2002), or using a
range-finder in the field. We placed five
transects perpendicular to the long axis of
the reservoir at 1/6, 2/6, 3/6, 4/6, and 5/6 the
length of the long axis (Figure 1). We
surveyed 20 points along each transect, one
point located one meter from the interface of
water and land on each side of the reservoir
and the remaining 18 spaced evenly on the
transect line.
For deeper (> 5 m) reservoirs, we also used
the point-transect method. Our sampling
was restricted to low-gradient near-shore
slopes, because we assumed these were most
likely to have established vegetation. We
determined locations of low gradient near-shore
slopes from a topographic map
(TOPO! 2002) or our own visual
examination at the reservoir. We selected
10 low-gradient slope locations around the
reservoir. We decided to select all stream
mouths and low-gradient areas near boat
ramps, because these were likely invasion
areas for invasive aquatic plants. We spread
the remaining sampling locations relatively
evenly around the reservoir shore to get a
representative sample of the reservoir. At
each location, we established a
perpendicular-to-shore transect originating
in the approximate center of the shoreline of
the low-gradient slope and extending out to
the edge of the aquatic weed bed, or out to
three meters deep if the water was turbid and
Figure 1. Diagram of transect layout for an
aquatic plant survey of a shallow lake (mean depth
< 5 m).
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
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we could not see the edge of the aquatic
weed bed. We sampled aquatic plants at 10
points beginning one meter from the water-land
interface, and the remaining nine
located equidistant from each other.
A total of 100 points were sampled at each
reservoir except at the following reservoirs:
20 points at Big Springs Pond because of its
small size (0.4 ha) and at Marshall Lake one
point was accidentally missed so only 99
points were sampled. Because of the large
size (over 1,200 ha) of Topock Marsh, we
added additional transects to acquire a better
sample of the aquatic plants in this water
body. At each sample point on each
transect, we used a rake (Wolf Garten DO-M
35) with a three-meter-long extendable
pole (Wolf Garten, Vario ZM-V3) to collect
aquatic plants, which restricted our
maximum sampling depth to approximately
3.3 meters. Aquatic plants were found on
occasion to be at depths greater than 3.3
meters, depending on water turbidity. The
rake head was lowered to the bottom and
rotated 360º and then pulled to the surface
(Gibbons et al. 1999). We recorded all taxa
of aquatic macrophytes collected on the rake
head. After all points on all transects were
sampled, we did a roving survey around the
reservoir to identify and record any species
not found on transects. We collected a
sample of each species for species
identification by a university botanist. We
typically took digital photographs of each
aquatic plant species at each reservoir.
We identified aquatic vascular plants to
species whenever possible. We did not
identify all algae to species, so they were
categorized into general groups (e.g.,
filamentous, encrusting), except for
muskgrass and stoneworts, which were
identified to genus. Cattails were typically
identified to genus level. Terrestrial plants
found along transects are not reported in this
paper. For each aquatic plant species, we
calculated prevalence (number of reservoirs
with a species divided by the total number of
reservoirs surveyed, multiplied by 100),
percent frequency of occurrence (number of
points with a species divided by the total
number of points sampled, multiplied by
100), and percent composition (number of
points with a taxa divided by the total
number of points with plants, multiplied by
100). For shallow reservoirs, percent
frequency of occurrence derived from our
point-transect methodology provides an
estimate of percent cover for each species
(Madsen 1999, Elzinga et al. 2001).
We used forward step-wise logistic
regression (SPSS 2003) to assess if
elevation, average depth, and average area
were significant predictors of species
occurrence. Variables were added or
removed from the models by using
likelihood ratio tests with a significance
level of 0.05. We assessed goodness of fit
of the models by examining –2 times the log
of the likelihood (-2 LL), where the best
model among those considered was the
model with the smallest –2 LL value (Manly
et al. 2002). Elevation, average surface
area, and average depth of reservoirs were
obtained from a Department fisheries
database. All reservoirs surveyed were
included in the logistic regression analyses,
except Lake Pleasant, which experiences
large seasonal fluctuations in water
elevations because it is a water storage
reservoir, which we thought resulted in an
absence of any aquatic vegetation.
To assess if our data supported
biogeographic theory that the number of
species increases with area, we assessed
relationships between number of aquatic
plant species (in categories submersed,
floating, emergent, or total) found in shallow
reservoirs and average surface area
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
5
(hectares) with linear regression. Surface
area was log transformed prior to analysis
and was regressed against number of
species, and in separate analyses, log-transformed
number of species. We only
examined shallow reservoirs to try to control
for the fact that most rooted species are
limited to shallow waters, and a few of the
deeper reservoirs are used for flood control
and have widely variable surface elevations
throughout the year.
We examined associations between pairs of
species with two-way contingency table
analysis and the phi coefficient (Zar 1984).
We restricted the analysis to species that
were found at five or more reservoirs.
EVALUATION OF HARVESTING
PROGRAM
Study Sites
We monitored vegetation coverage and
water quality before and after harvesting at
four reservoirs during 2005 and four
reservoirs during 2006. We wanted to
monitor two reservoirs harvested by the
large harvester (H-620) and two reservoirs
harvested by the small harvester (HM-220)
each year. However, because drought
conditions resulted in low reservoir levels,
several reservoirs were inaccessible,
particularly to the larger H-620 harvester.
Therefore, only one reservoir (Luna Lake)
harvested with the H-620 was monitored,
but it was monitored both in 2005 and in
2006. We monitored five reservoirs that
were harvested with the HM-220: Pena
Blanca Lake, Parker Canyon Lake, and
Crescent Lake during 2005, and Parker
Canyon Lake, Rainbow Lake, and Cluff
Ranch Pond #3 during 2006. For each
reservoir, we designated an area not to be
harvested (control area) and an area that
would be harvested (treatment area). We
also attempted to measure the numbers of
fish incidentally collected by the harvesters
at these six reservoirs, but, due to time
constraints, we only conducted this sampling
at five reservoirs: Pena Blanca Lake during
2005, and Luna Lake, Parker Canyon Lake,
Cluff Ranch Pond #3, and Rainbow Lake
during 2006.
For angler surveys, we monitored angler
attitudes at nine reservoirs (six were
reservoirs where we monitored vegetation
coverage and water quality) during 2006:
Arivaca Lake, Pena Blanca Lake, Parker
Canyon Lake, Cluff Pond #3, Nelson
Reservoir, Crescent Lake, Luna Lake,
Rainbow Lake, and Concho Lake. Our
vendor did not get our kiosks constructed on
time to deploy them during 2005, so we only
collected angler survey data during 2006.
Aquatic Vegetation Coverage
We used aquatic vegetation coverage as a
measure of angler access. The percent of
the lake surface area with aquatic vegetation
at or near the surface was visually estimated
during each water quality survey (see below)
while traveling around the lake in a boat,
and the areas with plants were shaded in on
a topographic map. In addition, surface area
coverage of aquatic macrophytes was
estimated with Geographic Information
Systems (GIS) technology. With our
Garmin eMAP GPS receiver turned on, we
piloted the boat along the edge of the
macrophytes bed, and saved the resulting
track within the GPS unit. We used the
reservoir shore shown on 7.5 minute series
U.S. Geological Survey topographic maps
(digitized into GIS) to calculate the total
surface area of the lake. We used our tracks
to determine the area of open water on the
lake and subtracted that area from the total
surface area to determine the surface area
covered by aquatic vegetation. We then
calculated percent of the surface area that
was covered by aquatic vegetation at each
lake during each survey.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
6
Fish Kills
We used the number of dead fish observed
during the aquatic vegetation surveys as a
fish kill index.
Water Chemistry
We used a before-after control-treatment
design, with before referring to before
harvesting and after referring to after
harvesting. Within each lake, we designated
a treatment cove and a control cove, each of
which had similar aquatic vegetation cover
and similar depths; control coves were not
harvested, whereas treatment coves were
harvested. We measured water quality
variables periodically (monthly during 2005
and every two weeks during 2006) before
and after harvesting; we attempted to have at
least three pre-harvesting and three post-harvesting
sampling events at each lake.
Mid-Day Sampling
We established a transect across the middle
of the aquatic macrophyte bed,
perpendicular to the long axis of each
treatment and control cove. We established
two other transects in open water (open-water
transects) 20-50 m from and parallel
to the aquatic plant bed transects. During
2006, we decreased the open-water transects
from two to one, and located it in the center
of the open-water portion (area absent of
aquatic macrophytes) of the lake. We
measured water chemistry variables at
points located on transects at 0.25, 0.5, and
0.75 transect length between 11:00 h and
14:30 h.
At each point, we measured water
temperature (°C), dissolved oxygen (mg/L
and % saturation), and pH at 1-meter depth
with a YSI 6920 multiparameter sonde
connected to a 610-DM Display/Logger
during 2005, or with a Hydrolab Reporter
multiparameter sonde connected to a
Hydrolab Surveyor 3 during 2006. We
measured alkalinity (mg/L of CaCO3) of a
100-ml water sample collected from the
surface with a Hach Model 16900 digital
titrator kit (brom-cresol green-methyl red
endpoint, sulfuric acid titrant). We
measured turbidity (NTU) of a water sample
collected from the surface with a HF
Scientific, Inc. DRT-15CE turbidimeter.
We used a secchi disk lowered into the
water on the shadowed side of the boat to
measure water clarity. To measure nitrate-nitrite
nitrogen (mg/L) and orthophosphate
(mg/L) concentrations, we collected a 100-
ml sample from immediately below the
water surface at each point and combined all
three samples from each transect into one
composite sample, and used a Hach DREL
2000 spectrophotometer to measure nitrate-nitrite
nitrogen (cadmium reduction method)
and orthophosphate (ascorbic acid method)
of the composite sample. We did not
sample nitrate and orthophosphate during
2006 because values from 2005 were highly
variable and many samples had undetectable
concentrations. To measure chlorophyll a
concentrations, a 100-ml sample was
collected from immediately below the water
surface at each point and all three samples
from each transect were combined into one
composite sample. We filtered he
composite-water samples onsite through
Whatman 47 mm glass microfibre filters,
wrapped them in aluminum foil, placed
them on ice, and transferred them to a
freezer until laboratory chlorophyll analysis
could be performed. We used the
spectrophotometric method (APHA et al.
2005) to determine chlorophyll a (μg/L)
content of samples.
22-Hour Sonde Sampling
Because pH and dissolved oxygen
measurements derived from the mid-day
sampling in 2005 were highly variable and
changes in levels after harvesting were not
very obvious, we measured these variables
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
7
every 2 h for 22 h during each sampling
event in 2006 in an attempt to account for
diel cycles and reduce measurement
variability; 22-h sonde sampling and mid-day
sampling co-occurred in 2006. We
fixed a buoy in place at the mid-point of
each treatment and control transect at each
lake. During each sampling event, we
affixed a Hydrolab Recorder sonde to the
bouy such that the probes were at 1-m depth;
we set the sonde to record water temperature
(°C), dissolved oxygen (mg/L and %
saturation), and pH every 2 h for 22 h.. We
pulled the sondes the following day and
downloaded data to a computer.
Analysis
We examined levels of dissolved oxygen,
pH, and nutrients because they have direct
impacts on fisheries and fisheries
management, and aquatic plants were
thought to affect levels of these variables.
Summer kills occur in high elevation
reservoirs in Arizona, typically as a result of
extended periods of low dissolved oxygen
concentrations. If aquatic plant biomass is
excessive, nighttime respiration by these
plants can consume most of the dissolved
oxygen in the water to levels less than 1-2
mg/L, and on overcast days, oxygen can
remain depleted into the daytime, thus
stressing and killing fish. Therefore, we
assessed if dissolved oxygen concentrations
increased following harvesting. With
respect to pH, the Department will not stock
trout if a water body has a pH greater than 9,
therefore this value was used as a criterion
to judge whether harvesting allowed for an
extended stocking period. Aquatic plants
store nitrogen and phosphorus in their
tissues, which are released when they die
and decompose, which may then increase
algal blooms. Therefore, we examined if
nutrient levels decreased following removal
of aquatic vegetation, and whether algal
chlorophyll a concentrations increased
following harvesting.
Water quality measurements for treatment
and control coves by sampling event were
plotted for each lake monitored and graphs
were examined to determine if there were
obvious changes in trends after harvesting.
Data used in the graphs were means (water
temperature, dissolved oxygen, pH, and
alkalinity) or raw values (nitrate,
orthophosphate, and chlorophyll a
concentrations). We also used intervention
analysis (SPSS 2003), a type of
Autoregressive Integrated Moving Average
(ARIMA) trend analysis, to assess if
harvesting (the intervention) affected the
trend in water chemistry measurements
differently for the treatment and control
coves. Autocorrelation and partial
autocorrelation graphs for treatment and
control groups were examined for each
water chemistry variable to decide which
ARIMA model to use. If treatment and
control coves were similar and harvesting
had an effect, then we expected water
quality trends within the treatment and
control coves to be similar prior to
harvesting, but divergent after harvesting.
Operational Cost of Harvesting
We acquired Harvesting Completion
Reports from Arizona Game and Fish
Department’s Aquatic Weed Harvesting
Program and input data to create an
electronic database. Data on Completion
Reports included: lake name, operator name,
date started and completed, duration and
monetary cost of labor, per-diem costs,
duration harvester and vehicles were
operated and associated costs, hours
harvester could not be operated and reason it
could not be operated (e.g., thunderstorm,
mechanical breakdown), miscellaneous
operational costs, total cost, estimated tons
or acres harvested, and harvester equipment
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
8
identification number; durations were
recorded in hours and costs were dollar
amounts. From this data we calculated tons
harvested per hour and cost per ton or acre
harvested.
We graphed total cost of the harvesting
program by year to assess monetary cost of
the program over time. We assessed if there
were relationships between hours the
harvester was operated and tons harvested
and total costs with Pearson’s correlation
coefficient (Zar 1984). We wanted to be
able to assess the hypothesis that yearly
harvesting would deplete the nutrients in a
lake and result in less plant biomass in
successive years. However, data on yearly
plant coverage or biomass were nonexistent.
Therefore, we addressed the hypothesis
indirectly by graphing tons harvested by
year at each lake and visually examining
graphs to determine if there were downward
trends in tons harvested from year to year.
Incidental Fish Collection
Small fish are reported to be inadvertently
harvested with weeds (Wile 1978, Haller et
al. 1980, Mikol 1985, Engel 1990, Booms
1999), so we examined the numbers and
biomass of fish removed by harvesting. At
five reservoirs, three 0.3 m3 subsamples (a
wheel barrel load) of harvested weeds were
picked through and fish extracted, identified
to species, counted, and weighed (g wet
weight total sample by species). Monetary
loss to the fishery was estimated by
assigning a monetary value to each fish
using American Fisheries Society’s (AFS)
Investigation and Valuation of Fish Kills
book (AFS 1992).
Angler Use Survey
We used angler survey cards to evaluate
angler opinions of aquatic vegetation and
aquatic vegetation control. Five questions
were presented to anglers on the angler
survey card (Figure 2). Questions 1 and 2
were used to assess if aquatic vegetation
affected the angler’s fishing experience or
prevented them from fishing entirely.
Question 3 was used to assess how much of
the lake was perceived to be inaccessible
because of aquatic vegetation (i.e., an
estimate of the aquatic plant coverage).
Question 4 was used to assess angler’s
attitudes towards aquatic vegetation control.
Question 5 was used to determine if the
person was a casual angler (only fished a
few times a year) or an avid angler (fished
15 or more days a year) at that particular
lake.
A kiosk with survey cards and a drop box
was placed at each of nine reservoirs in
February 2006; four reservoirs (Arivaca
Lake, Pena Blanca Lake, Cluff Ranch Pond
#3, and Parker Canyon Lake) were at
elevations between 1,008 and 1,642 m, and
five reservoirs (Concho Lake, Crescent
Lake, Luna Lake, Nelson Reservoir,
Rainbow Lake) were at elevations between
1,919 and 2,757 meters. Five of the
reservoirs were harvested during 2006
(Cluff Ranch Pond #3, Luna Lake, Parker
Canyon Lake, Pena Blanca Lake, and
Rainbow Lake), the other four were
harvested at least once during 2000-2005.
The kiosks were visited monthly to retrieve
Figure 2. Angler survey card used during study.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
9
completed cards and replenish with blank
survey cards.
Responses were entered into a computer as
numeric variables to facilitate statistical
analyses: for question one, 1 = greatly
hinders, 2 = moderately hinders, 3 = no
effect, 4 = moderately improves, and 5 =
greatly improves; for question two, 1 = yes
and 2 = no; for question three, 1 = 0%, 2 =
1-25%, 3 = 26-50%, 4 = 51-75%, and 5 =
76-100%; for statement four, 1 = strongly
disagree, 2 = moderately disagree, 3 = no
opinion, 4 = moderately agree, and 5 =
strongly agree. For questions 1-4, the
percentage of anglers, statewide and at each
lake that marked each response was
calculated. Data from Crescent Lake and
Concho Lake are not presented because too
few anglers responded to the survey (3 and
10 anglers respectively). We assessed if
angler responses to questions 1-4 at the five
reservoirs that were harvested during 2006
differed during the period before harvesting
from the period after harvesting. We also
used Pearson’s correlation (Zar 1984) to
assess potential relationships among
responses to the four questions and the
number of days per year that an angler
fished that reservoir.
RESULTS
STATEWIDE AQUATIC PLANT
SURVEY
We sampled 38 reservoirs within 29 HUCs
in Arizona from 2004 to 2006 (Figure 3).
We did not reach our target of sampling a
reservoir in the 45 available HUCs due to
the drying of reservoirs in four HUCs, lack
of access on tribal lands in three HUCs,
rough road conditions in one HUC, and
international border issues in one HUC. We
did not sample reservoirs in seven other
HUCs because of time and budgetary
constraints. We surveyed 17 of the 27
reservoirs that have been harvested by the
Department.
During this study, the most prevalent taxa
were filamentous algae, being present at
76% of the sampled reservoirs (Table 2,
Appendix A1) and another alga taxon,
muskgrass, found at 53% of the sites
surveyed. The most prevalent vascular plant
species were coontail, sago pondweed,
cattails, and hard-stem bulrush, which were
found in 42% to 47% of the reservoirs
surveyed. Other species commonly found
(prevalence 26% to 37%) in our surveys
were northern watermilfoil, water knotweed
(Polygonum amphibium), two-leaf elodea
(Elodea bifoliata), and small pondweed
(Potamogeton pusillus). Two non-native
aquatic macrophyte species were found on
transects during our surveys: Eurasian
watermilfoil and curly-leafed pondweed.
Figure 3. Map of reservoirs surveyed for aquatic
vegetation in Arizona from 2004 to 2006.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 0
Table 2. Aquatic plant taxa found in 38 Arizona reservoirs during 2004 through 2006 surveys, giving plant type (E = emergent, F =
floating, S = submersed), prevalence (NP = number of reservoirs with taxa present, and %P = percent of reservoirs with taxa present),
mean percent composition (NC = number of reservoirs with taxa found on transect points, and %C = number of points with taxa
divided by total number of points with plants), and for shallow reservoirs, mean percent frequency of occurrence (NF = number of
shallow reservoirs with taxa present, and %F = number of points with taxa divided by total number of points sampled in shallow
reservoirs). Also given are the minimum and maximum reservoir elevation (m), minimum and maximum average reservoir depth (m),
and minimum and maximum average reservoir surface area (ha). NP is greater than NC when taxa were not found on transects but
were found during the roving survey after transect sampling was complete. Standard deviations of means are given in parentheses.
Prevalence Composition Frequency
Taxa Type
Min
Elev.
Max
Elev.
Min
Depth
Max
Depth
Min
Area
Max
Area NP %P NC %C NF %F
Azolla filiculoides F 1,567 1,567 15.2 15.2 8.1 8.1 1 2.6 1 7.1 1 6.0
Bacopa monnieri E 23 23 2.4 2.4 131.5 131.5 1 2.6 . .
Carex spp. E 2,403 2,403 2.4 2.4 30.4 30.4 1 2.6 1 1.3 1 1.0
Carex stipata E 2,664 2,664 13.7 13.7 4.5 4.5 1 2.6 . .
Ceratophyllum demersum S 23 2,403 0.9 27.4 4.0 283.3 16 42.1 16 48.0 (32.8) 13 37.0 (24.8)
Chara spp. S 23 2,664 0.9 27.4 0.4 1295.0 20 52.6 20 21.8 (26.0) 19 17.8 (23.1)
Crypsis schoenoides E 1,567 1,567 15.2 15.2 8.1 8.1 1 2.6 1 2.4 1 2.0
Cyperus esculentus E 1,685 1,685 12.2 12.2 22.3 22.3 1 2.6 1 2.2 0 .
Cyperus odoratus E 1,642 1,642 25.0 25.0 50.6 50.6 1 2.6 1 2.1 0 .
Cyperus spp. E 55 2,168 0.9 73.2 14.2 1092.7 4 10.5 3 18.3 (28.3) 1 1.0
Echinochloa crus-galli E 1,685 2,143 1.8 12.2 22.3 1295.0 3 7.9 1 6.7 0 .
Eleocharis palustris E 1,488 2,403 0.9 15.2 0.4 1295.0 8 21.1 8 3.1 (2.1) 8 2.6 (1.8)
Eleocharis parishii E 1,567 2,044 1.8 15.2 8.1 32.4 2 5.3 2 1.1 (0.1) 2 1.0 (0.0)
Eleocharis spp. E 2,664 2,664 13.7 13.7 4.5 4.5 1 2.6 1 1.6 1 1.0
Elodea bifoliata S 1,567 2,757 0.9 15.2 4.0 1295.0 11 28.9 11 43.5 (28.3) 11 38.3 (25.2)
Filamentous algae 23 2,757 0.9 25.0 0.4 1295.0 29 76.3 28 30.3 (29.6) 24 24.9 (26.2)
Glyceria grandis E 2,044 2,403 1.8 2.4 30.4 32.4 2 5.3 1 2.5 1 2.0
Juncus effuses E 2,113 2,113 13.4 13.4 2.8 2.8 1 2.6 0 . 0 .
Lemna minor F 335 1,700 1.8 24.4 2.0 1092.7 3 7.9 3 2.9 (2.2) 2 2.5 (2.1)
Myriophyllum sibiricum S 1,008 2,757 0.9 15.2 1.2 1295.0 14 36.8 13 53.1 (32.7) 12 37.3 (27.4)
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 1
Prevalence Composition Frequency
Taxa Type
Min
Elev.
Max
Elev.
Min
Depth
Max
Depth
Min
Area
Max
Area NP %P NC %C NF %F
Myriophyllum spicatum S 335 2,664 1.8 25.0 4.5 1092.7 10 26.3 9 55.8 (34.5) 5 39.6 (26.5)
Najas guadalupensis S 1,567 1,567 15.2 15.2 8.1 8.1 1 2.6 1 3.6 1 3.0
Najas marina S 23 1,567 1.8 27.4 4.0 1214.1 8 21.1 8 56.6 (42.5) 6 46.9 (26.1)
Nitella spp. S 977 1,642 4.6 25.0 4.0 50.6 2 5.3 2 11.8 (13.7) 1 17.0
Phragmites australis E 23 583 2.4 73.2 131.5 1039.3 3 7.9 3 16.4 (17.2) 2 3.5 (0.7)
Polygonum amphibium E 1,488 2,403 0.9 15.2 4.0 1295.0 13 34.2 10 11.6 (10.0) 10 8.9 (7.0)
Polygonum argyrocoleon E 1,685 1,685 12.2 12.2 22.3 22.3 1 2.6 1 2.2 0 .
Polygonum lapathifolium E 1,168 2,143 12.2 25.0 18.2 105.2 4 10.5 0 . 0 .
Polygonum spp. E 55 583 3.0 73.2 259.0 1039.3 2 5.3 1 2.8 0 .
Pontederia spp. E 2,044 2,044 1.8 1.8 32.4 32.4 1 2.6 1 7.1 1 7.0
Potamogeton crispus S 23 1,700 1.8 2.4 2.0 131.5 2 5.3 1 86.5 1 64.0
Potamogeton foliosus S 1,567 1,567 15.2 15.2 8.1 8.1 1 2.6 1 1.2 1 1.0
Potamogeton pusillus S 23 2,664 1.8 19.8 1.2 1295.0 10 26.3 9 19.7 (26.1) 8 16.9 (21.4)
Rannunculus longirostris S 2,168 2,664 0.9 13.7 1.2 30.4 5 13.2 5 5.4 (3.3) 5 4.4 (2.6)
Rorippa nasturtium-aquaticum E 2,117 2,117 0.9 0.9 0.4 0.4 1 2.6 1 11.1 1 10.0
Schoenoplectus acutus E 23 2,664 0.9 25.0 2.0 1214.1 17 44.7 13 9.8 (10.4) 12 7.1 (8.9)
Scirpus microcarpus E 2,664 2,664 13.7 13.7 4.5 4.5 1 2.6 0 . 0 .
Sparganium spp. E 2,047 2,047 3.0 3.0 68.8 68.8 1 2.6 1 1.0 1 1.0
Spirodela polyrhiza F 1,168 1,168 19.8 19.8 18.2 18.2 1 2.6 1 4.0 0 .
Stuckenia filiformis S 951 951 6.1 6.1 13.0 13.0 1 2.6 1 2.0 1 1.0
Stuckenia pectinatus S 23 2,757 0.9 15.2 4.0 1295.0 16 42.1 16 42.2 (29.1) 16 36.4 (28.6)
Typha spp. E 23 2,259 1.2 73.2 2.0 1214.1 18 47.4 15 22.1 (27.2) 11 5.1 (4.1)
Veronica anagallis-aquatica E 1,567 1,567 15.2 15.2 8.1 8.1 1 2.6 1 1.2 1 1.0
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 2
Eurasian watermilfoil was found at nine
(24%) of the reservoirs we surveyed; curly-leafed
pondweed was found at two (5%) of
the reservoirs we surveyed, but at one of
these reservoirs curly-leafed pondweed was
not found on a transect but was seen floating
at the boat ramp. Some of the plants in this
study were not identified to species because
of lack of identifying structures such as
seeds or flowers. In four surveyed
reservoirs, we detected no aquatic
macrophytes in the water: Cataract Lake,
Knoll Lake, Lake Pleasant, and Woods
Canyon Lake (Woods Canyon Lake and
Cataract Lake had emergent taxa along the
bank).
A few of the aquatic macrophyte species
dominated (percent compositions greater
than 50%) the species assemblage at study
reservoirs where they were found (Table 2).
Curly-leafed pondweed, an invasive
nonnative, dominated (87% composition)
the assemblage at the one reservoir where it
was found on transects. Eurasian
watermilfoil, also an invasive nonnative,
was the most dominant aquatic macrophyte
at four of the eight reservoirs where it was
found on transects and had a mean
composition of 61% at these eight
reservoirs. For native species, the most
dominant species was spiny naiad, which
was found on transects at eight reservoirs
(mean composition of 57%) and was the
dominant aquatic macrophyte at five of
those reservoirs (> 75% composition).
Spiny naiad was dense in backwater
reservoirs along the Colorado River such as
Martinez Lake near Yuma, Arizona and
Topock Marsh near Kingman, Arizona.
Northern watermilfoil was the next most
dominant native species, being the most
common plant at 8 of the 13 reservoirs
where it was found, with a mean
composition of 54%. Several other native
species that tended to dominate the aquatic
plant communities included coontail
(dominated at 5 of 16 reservoirs where it
was present and had a mean percent
composition of 48%), two-leaf elodea
(dominated at 3 of 11 reservoirs where it
was present and had a mean percent
composition of 44%), and sago pondweed
(dominated at 5 of 16 reservoirs where it
was present and had a mean percent
composition of 42%). Several taxa
mentioned above were especially abundant
with percent compositions in excess of 90%
at nine reservoirs: coontail, spiny naiad,
sago pondweed, northern watermilfoil,
Eurasian watermilfoil, and filamentous
algae.
We detected 20 significant (p < 0.05)
positive associations (co-occurrence)
between pairs of aquatic plant taxa (Table
3). For taxa groupings with more than two
species, the most common aquatic plant
assemblage in Arizona reservoirs was
comprised of two-leaf elodea, water
knotweed, and coontail; this assemblage was
found at ten reservoirs. An assemblage
comprised of these three species plus sago
pondweed was found at six reservoirs.
Other groupings of more than two taxa were
less common. We also detected two
negative associations (Table 3): cattail with
creeping spikerush (Eleocharis palustris),
and Eurasian watermilfoil with muskgrass.
Results of logistic regressions indicate that
average depth and elevation were significant
predictors of species occurrence (Appendix
A2). Average depth was a significant
predictor of occurrence for two species:
water knotweed and sago pondweed. Both
were more likely to be found in reservoirs
that were shallow than those that were deep.
Average depth was not a significant
predictor of occurrence for other species
examined. Elevation was a significant
predictor of species occurrence for six
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 3
Table 3. Co-occurrence (phi coefficient: Zar 1984) of aquatic plant taxa found in Arizona reservoirs during surveys 2004 through
2006. Significant (P < 0.05) phi coefficients are indicated with an asterisk; N = 38 reservoirs. Coefficients with water buttercup, and
hard-stem bulrush were not significant and are not shown.
Coontail Muskgrass
Two-leaf
elodea
Creeping
spikerush
Filamentous
algae
Northern
watermilfoil
Eurasian
watermilfoil
Spiny
naiad
Water
knotweed
Small
pondweed
Sago
pondweed
Muskgrass Φ2 0.275
P 0.094
Two-leaf elodea Φ2 0.513 0.141
P 0.001* 0.400
Creeping spikerush Φ2 0.213 0.102 0.382
P 0.198 0.542 0.018*
Filamentous algae Φ2 0.350 0.339 0.356 0.288
P 0.031* 0.037* 0.028* 0.080
Northern watermilfoil Φ2 0.233 0.178 0.355 0.141 0.297
P 0.160 0.284 0.029* 0.399 0.070
Eurasian watermilfoil Φ2 0.152 -0.339 0.054 0.016 0.019 -0.297
P 0.363 0.037* 0.748 0.924 0.909 0.070
Spiny naiad Φ2 -0.048 0.361 -0.187 -0.108 -0.168 -0.261 -0.136
P 0.774 0.026* 0.260 0.517 0.314 0.114 0.416
Water knotweed Φ2 0.621 0.240 0.763 0.444 0.271 0.484 0.120 -0.236
P 0.000* 0.147 0.000* 0.005* 0.100 0.002* 0.472 0.153
Small pondweed Φ2 0.096 0.208 0.014 0.131 0.333 0.163 0.089 -0.015 -0.053
P 0.568 0.210 0.934 0.433 0.041* 0.328 0.596 0.927 0.752
Sago pondweed Φ2 0.352 0.382 0.513 0.344 0.350 0.343 -0.224 0.344 0.509 -0.147
P 0.030* 0.018* 0.001* 0.034* 0.031* 0.035* 0.176 0.034* 0.001* 0.380
Cattail Φ2 -0.169 -0.050 -0.257 -0.361 -0.215 -0.178 -0.157 0.286 -0.240 0.031 -0.062
P 0.312 0.766 0.119 0.026* 0.194 0.284 0.348 0.082 0.147 0.851 0.712
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 4
species. Cattails and spiny naiad were more
likely to occur at low elevation reservoirs
than at high elevation reservoirs, whereas
two-leaf elodea, northern watermilfoil, water
knotweed, and water buttercup
(Rannunculus longirostris) were more likely
to occur at high elevation reservoirs than at
low elevation reservoirs. Eurasian
watermilfoil, coontail, small pondweed,
creeping spikerush, and hard-stem bulrush
occurred at broader ranges of elevations;
therefore elevation was not a significant
predictor of occurrence for these species.
Average surface area was not a significant
predictor of occurrence for any single
species examined. Average surface area
(log transformed) was however,
significantly related to the number (log
transformed) of submersed aquatic plant
taxa found at reservoirs [log species = 0.386
+ 0.117(log area), r2 = 0.151, df = 1, 24, p =
0.05]; no relationships between surface area
and number of emergent species or total
species were statistically significant.
Reservoirs where aquatic vegetation was
harvested tended to have several species in
common. For example, half (nine) of the
harvested reservoirs that were surveyed for
aquatic plants had Eurasian watermilfoil
present. Interestingly, 9 of the 10 reservoirs
with Eurasian watermilfoil present were
harvested, suggesting that harvesting
operations have spread this plant among
reservoirs. Filamentous alga was found in
all 17 of the harvested reservoirs that we
surveyed, but was also found at 10 of the
reservoirs that were not harvested. Several
other species common in harvested
reservoirs were also common in non-harvested
reservoirs: two-leaf elodea (in
eight harvested and eight non-harvested
reservoirs), coontail (in 10 harvested and 6
non-harvested reservoirs), muskgrass (in 7
harvested and 13 non-harvested reservoirs),
and northern watermilfoil (in six harvested
and eight non-harvested reservoirs).
EVALUATION OF HARVESTING
PROGRAM
Aquatic Vegetation Coverage
A decrease in estimated percent aquatic
plant coverage from immediately before
harvesting to after immediately harvesting
was evident for six harvesting events (Figure
4). Estimated vegetation coverage
decreased from 74% immediately before to
49% immediately after harvesting at Luna
Lake during 2005, from 73 to 67% at Luna
Lake during 2006, from 27 to 3% at Parker
Canyon Lake during 2006, from 40 to 38%
at Crescent Lake during 2005, from 60 to
41% at Cluff Pond #3 during 2006, and from
53 to 30% at Rainbow Lake during 2006.
Estimated vegetation coverage increased
from immediately before to immediately
after harvesting at Parker Canyon during
2005 (from 18 to 20%), and at Pena Blanca
Lake during spring 2005 (22 to 28%).
During 2006, the decreases in aquatic
macrophyte cover at each lake during or
immediately following the harvesting period
was partly due to increases in lake depth
because of precipitation and runoff. For
example, we estimated that lake levels
increased approximately 2 m at Parker
Canyon Lake, 1 m at Luna Lake, 1 m at
Cluff Pond #3, and 2 m at Rainbow Lake.
The change in lake levels not only decreased
macrophyte surface cover, but also reduced
the efficiency of the harvesters because the
operators harvest where they can see
macrophytes on or near the water surface
and the machines have a maximum cutting
depth of 1.68 meters.
In the reservoirs that we monitored water
chemistry and aquatic plant coverage,
harvesters removed: 70 and 247.5 tons from
Pena Blanca Lake during 2005, 12.5 tons
from Crescent Lake during 2005, 160 and 50
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 5
tons from Parker Canyon Lake during 2005
and 2006 respectively, 756 and 336 tons
from Luna Lake during 2005 and 2006
respectively, 9 tons from Cluff Pond during
2006, and 28 tons from Rainbow Lake
during 2006.
Fish Kills
For all reservoirs monitored, we observed
few dead fish during the aquatic vegetation
coverage surveys (Figure 5). A pairwise t-test
comparing the average number of dead
fish observed per survey before versus after
harvesting at the eight reservoirs was
insignificant (before = 1.44 dead fish, after =
0.88 dead fish, t = 0.74, df = 7, p = 0.459),
likely because most observations were of
zero dead fish observed. Based on an
examination of the graphs it appears that on
average, more dead fish were observed
before harvesting than after harvesting at
five reservoirs: Luna Lake and Crescent
Lake during 2005 and Rainbow Lake, Cluff
Pond #3, and Parker Canyon Lake during
2006; the difference is slight at Parker
Canyon Lake during 2006, but number of
dead fish observed declined through the
harvesting period but spiked afterward. At
Parker Canyon Lake during 2005 and Luna
Lake during 2006 fewer, on average, dead
fish were observed before than after
harvesting. No clear pattern was evident for
Pena Blanca Lake during 2005.
Water Chemistry
We had hypothesized that water quality
measures from treatment (harvested) and
control (not harvested) locations would be
similar before harvesting and then would
diverge subsequent to harvesting. Based on
examination of graphs (Figures 6-13) and
ARIMA trend analyses, water quality
measures for treatment and control locations
were similar during the pre-treatment period,
or if different, then usually trended in the
same direction. However, divergence in
0
20
40
60
80
100
Luna Lake
2006
0
20
40
60
80
100
Luna Lake
2005
0
20
40
60
80
100
Parker
Canyon
Lake 2005
0
20
40
60
80
100
Parker
Canyon
Lake 2006
Estimated Percent Aquatic Vegetation Coverage
0
20
40
60
80
100
Cresent Lake
2005
0
20
40
60
80
100
Cluff Pond 3
2006
Month
0
20
40
60
80
100
Pena Blanca
Lake 2005
Apr May Jun Jul Aug Sep Oct Nov
0
20
40
60
80
100
Rainbow Lake
2006
Harvesting period
Figure 4. Monthly (2005) and bi-monthly
(2006) estimates of percent aquatic macrophytes
coverage (derived from GPS routes along edge
of macrophytes beds) at each lake monitored
during 2005 and 2006. Dotted vertical lines
represent the period during which aquatic plant
harvesting occurred.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 6
water quality measures between treatment
and control locations in the post-harvesting
period were not evident; that is, the
harvesting event was not a significant (p >
0.05) intervention in any ARIMA model.
For example, levels of pH for both the mid-day
sampling (Figure 6) and 22-hour sonde
sampling (Figure 7a) were very similar in
treatment and control locations after
harvesting. Dissolved oxygen
concentrations for both the 22-h sonde
sampling (Figure 7b) and the mid-day
(Figure 8) sampling also were very similar
in treatment and control locations following
harvesting, except in Luna Lake and
Crescent Lake during 2005 (Figure 8) where
concentrations increased in the treatment
coves immediately following harvesting and
decreased in the control coves, and 22-hour
sonde dissolved oxygen concentrations from
Luna Lake during 2006 (Figure 7b) were
different between control and treatment
coves both before and after harvesting with
no clear pattern that would indicate the
difference was a result of harvesting.
Nitrate and orthophosphate concentrations
(Figure 9) for mid-day sampling were
similar in treatment and control coves prior
to and after harvesting, or if they diverged,
then no pattern was evident. Harvesting also
did not appear to have an affect on
chlorophyll a concentrations (Figure 10),
alkalinity (Figure 11), water temperature
(Figure 12) or turbidity (Figure 13).
We evaluated potential relationships
between planktonic algae and water quality
variables with Parson’s correlation
coefficient (Zar 1984); we ran correlations
between chlorophyll a concentrations and
pH, water temperature, turbidity, and
concentrations of dissolved oxygen,
alkalinity, orthophosphates and nitrates for
each reservoir monitored each year. At most
lakes, correlations were not significant (p >
0.05). Chlorophyll a concentrations were
0
2 4 6
8 Luna Lake
2005
0
2 4
6 8
Luna Lake
2006
0
2 4
6 8
Parker
Canyon
Lake 2005
0
2
4 6 8
Parker
Canyon
Lake 2006
0
2
4 6 8
Cresent
Lake
2005
0
2 4 6 8
Cluff Pond 3
2006
Number of Dead Fish Observed
0
2
4 6 8
Pena Blanca
Lake 2005
Apr Jun Aug Oct
0
5
10
15
20 Rainbow Lake
2006
Harvesting period
Month
Figure 5. Number of dead fish observed during
aquatic vegetation coverage sampling at each lake
monitored during 2005 and 2006. Dotted vertical
lines represent the period during which aquatic
plant harvesting occurred. Note y-axis scales are
not all the same.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 7
pH
7
8
9
10
11
Control
Treatment
Luna Lake
2005
7
8
9
10
11
Luna Lake
2006
7
8
9
10
11
Parker C anyon
Lake 2005
7
8
9
10
11
Parker C anyon
Lake 2006
7
8
9
10
11
Crescent Lake
2005
7
8
9
10
11 Cluff Pond 3
2006
Month
7
8
9
10
11
Pena Blanca
Lake 2005
Apr Jun Aug Oct
7
8
9
10
11 Rainbow Lake
2006
Figure 6. Mean, with standard error bars, monthly
(2005) and bi-monthly (2006) mid-day pH of three
measurements from the treatment (harvested) and
control (not harvested) transects at each lake
monitored during 2005 and 2006. Dotted vertical
lines represent the starting and ending dates during
which aquatic plants were harvested.
7
8
9
10
11
Control
Treatment
Cluff Pond 3
2006
7
8
9
10
11
Luna Lake
2006
Month
pH
7
8
9
10
11
Parker Canyon
Lake 2006
Apr Jun Aug Oct
7
8
9
10
11
Rainbow Lake
2006
0
4
8
12
16 Control
Treatment
Cluff Pond 3
2006
0
4
8
12
16
Luna Lake
2006
Month
Dissolved Oxygen (mg\L)
0
4
8
12
16
Parker Canyon
Lake 2006
Apr Jun Aug Oct
0
4
8
12
16
Rainbow
Lake 2006
A
B
Figure 7. Mean, with standard error bars, daily
(A) pH and (B) dissolved oxygen concentration
measured every 2 h for 22 h at bi-weekly intervals
within treatment (harvested) and control (not
harvested) coves at four reservoirs during 2006.
Dotted vertical lines represent the period during
which aquatic plant harvesting occurred.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 8
0
4
8
12
16
Control
Treatment
Luna Lake
2005
0
4
8
12
16
Luna Lake
2006
0
4
8
12
16
Parker Canyon
Lake 2005
0
4
8
12
16
Parker Canyon
Lake 2006
0
4
8
12
16
0
4
8
12
16
Crescent Lake
2005
Cluff Pond 3
2006
Month
Dissolved Oxygen (mg\L)
0
4
8
12
16
Pena Blanca
Lake 2005
Apr Jun Aug Oct
0
4
8
12
16
Rainbow
Lake 2006
Figure 8. Mean, with standard error bars,
monthly (2005) and bi-monthly (2006) mid-day
dissolved oxygen concentration (mg/L) of three
measurements on the treatment (harvested) and
control (not harvested) transects at each lake
monitored during 2005 and 2006. Dotted vertical
lines represent the period during which aquatic
plants were harvested.
0.0
0.2
0.4
Control
Treatment
Crescent Lake
2005
0.00
0.03
0.06
0.09 Luna Lake
2005
Month
Nitrate (NO3
- -N mg/L)
0.00
0.03
0.06
0.09 Parker
Canyon
Lake 2005
Apr Jun Aug Oct
0.00
0.03
0.06
0.09 Pena Blanca
Lake 2005
0
1
2
3
Control
Treatment
Crescent Lake
2005
0
1
2
3
Month
Orthophosphate (PO4
3- mg/L)
0
1
2
3
Luna Lake
2005
Parker
Canyon
Lake 2005
Apr Jun Aug Oct
0
1
2
3
Pena Blanca
Lake 2005
A
B
Figure 9. Monthly mid-day nitrate (A) and
orthophosphate (B) concentrations (mg/L) of
three-part composite samples from the treatment
(harvested) and control (not harvested) transects
at four reservoirs during 2005. Dotted vertical
lines represent the period during which aquatic
plants were harvested. Note y-axis scales are not
all the same.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
1 9
0
40
80
120
160
Control
Treatment
Luna Lake
2005
0
40
80
120
160 Luna Lake
2006
0
4
8
12
Parker Canyon
Lake 2005
0
4
8
12 Parker
Canyon
Lake 2006
0
200
400 Crescent
Lake
2005
0
20
40
Cluff Pond 3
2006
Chlorophyll a (μg\L)
0
40
80
120 Pena Blanca Lake 2005
Apr Jun Aug Oct
0
40
80
120 Rainbow Lake
2006
Month
Figure 10. Monthly (2005) and bi-monthly (2006)
mid-day chlorophyll a concentrations of three-part
composite samples from treatment (harvested) and
control (not harvested) transects at each lake
monitored during 2005 and 2006. Dotted vertical
lines represent the period during which aquatic
plants were harvested. Note y-axis scales are not
all the same.
0
75
150
225
Control
Treatment
Luna Lake
2005
0
75
150
225 Luna Lake
2006
0
75
150
225 Parker
Canyon
Lake 2005
0
75
150
225 Parker
Canyon
Lake 2006
0
75
150
225 Crescent
Lake 2005
0
75
150
225 Cluff Pond 3
2006
Month
Alkalinity (mg/L CaCO3)
0
75
150
225 Pena Blanca
Lake 2005
Apr Jun Aug Oct
0
75
150
225 Rainbow Lake
2006
Figure 11. Monthly (2005) and bi-monthly (2006)
mid-day mean, with standard error bars, alkalinity
of three measurements from the treatment
(harvested) and control (not harvested) transects
at each lake monitored during 2005 and 2006.
Dotted vertical lines represent the period during
which aquatic plants were harvested.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 0
Water Temperature (oC)
0
10
20
30
Control
Treatment
Luna Lake
2005
0
10
20
30
Luna Lake
2006
0
10
20
30
Parker
Canyon
Lake 2005
0
10
20
30
Parker
Canyon
Lake 2006
0
10
20
30
Crescent
Lake 2005
0
10
20
30
Cluff Pond 3
2006
Month
0
10
20
30
Apr Jun Aug Oct
0
10
20
30
Pena Blanca
Lake 2005
Rainbow
Lake 2006
Figure 12. Monthly (2005) and bi-monthly (2006)
mid-day mean, with standard error bars, water
temperature of three measurements from the
treatment (harvested) and control (not harvested)
transects at each lake monitored during 2005 and
2006. Dotted vertical lines represent the period
during which aquatic plants were harvested.
0
50
100
150
200
Control
Treatment
Luna Lake
2005
0
10
20
30
Luna Lake
2006
0
10
20
30
Parker
Canyon
Lake 2005
0
10
20
30
Parker
Canyon
Lake 2006
0
40
80
120 Crescent
Lake 2005
0
10
20
30
Cluff Pond 3
2006
Month
Turbidity (NTU) 0
10
20
30
Pena Blanca
Lake 2005
Apr Jun Aug Oct
0
10
20
30
Rainbow
Lake 2006
Figure 13. Monthly (2005) and bi-monthly (2006)
mid-day mean, with standard error bars, turbidity
of three measurements on the treatment
(harvested) and control (not harvested) transects
at each lake monitored during 2005 and 2006.
Dotted vertical lines represent the period during
which aquatic plants were harvested. Note, y-axis
scales are not all the same.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 1
positively correlated with turbidity at Pena
Blanca Lake (r = 0.721, N = 13, p = 0.003),
Rainbow Lake (r = 0.847, N = 39, p <
0.001)), Luna Lake during 2005 (r = 0.936,
N = 10, p < 0.001), Luna Lake during 2006
(r = 0.649, N = 33, p < 0.001), and Parker
Canyon Lake during 2006 (r = 0.554, N =
33, p = 0.003). At Rainbow Lake there were
also significant correlations between
chlorophyll a concentrations and water
temperature (r = 0.343, N = 39, p = 0.033),
percent saturation dissolved oxygen (r =
0.335, N = 36, p = 0.046), and pH (r =
0.655, N = 39, p < 0.001), and at Luna Lake
during 2006 there was a positive correlation
between chlorophyll a concentration and
water temperature (r = 0.498, N = 33, p =
0.003).
Operational Cost of Harvesting
Tons of aquatic plants harvested was
positively associated with duration the
harvester was operated (r = 0.661, N = 161,
P < 0.001) and with total cost (r = 0.686, N
= 163, P < 0.001). Duration that the
harvester was operated was also positively
associated with total cost (r = 0.756, N =
238, P < 0.001). We did not detect any
consistent downward trends from year to
year in tons of aquatic plants harvested
(Figure 14). The Aquatic Weed Harvesting
Program expended approximately 1.3
million dollars from its inception in 1982
through 2006 (Figure 15; an average of
$50,601 per year); this amount does not
include the cost of the harvesters. Adding in
the purchase cost of the harvesters brings the
total to approximately 1.49 million dollars;
the H-650 cost ~$60,000, the H-620 cost
$103,500, and the HM-220 cost $62,000.
Incidental Fish Collection
Five fish species (two additional types not
fully identified) were found in our samples
(wheel barrel loads) of harvested weeds at
the five reservoirs examined (Table 4). All
0
500
1000
1500
Concho Lake
0
500
1000
1500
Crescent Lake
0
500
1000
1500
Luna Lake
0
500
1000
1500
Nelson Reservoir
0
500
1000
1500
Rainbow Lake
0
500
1000
1500
0
500
1000
1500
Year
1980 1985 1990 1995 2000 2005
0
500
1000
1500
Parker Canyon Lake
Pena Blanca Lake
Cluff Ranch Pond #3
Tons
0
500
1000
1500
Sunrise Lake
Figure 14. Estimated tons (wet weight) of aquatic
vegetation harvested each year in nine Arizona
reservoirs; 18 other reservoirs that were harvested in
fewer than 6 years or without estimates of tons
harvested are not shown.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 2
but one of the species, fathead minnow,
were sport fish. The ten unknown fish at
Pena Blanca Lake were originally identified
as trout because of observed dark vertical
bands (these fish were inadvertently not
collected and preserved), but given their size
(all < 30 mm TL) and month (May 27,
2005) captured, they may also have been
black crappie or sunfish. Fingerling trout
were stocked on March 15, so they should
have grown larger than 30 mm by May 27.
The sample for the one other unknown fish
from Parker Canyon Lake was lost, and no
description was written down. For all
reservoirs and samples, all fish found were
less than 101 mm TL, and 87% were less
than 50 mm TL.
Estimated number of fish entrapped in
aquatic weeds per harvester load ranged
from 57 (Rainbow Lake) to 818 (Cluff
Pond) for the smaller HM-220 harvester,
and 115 for the larger H-620 harvester used
at Luna Lake (Figure 16). Estimated cost of
fish per load ranged from $12 (Parker
Canyon Lake) to $138 (Cluff Pond) for the
smaller HM-220 harvester, and $9 for the
larger H-620 harvester used at Luna Lake.
The estimated total cost of fish harvested at
each lake, calculated by multiplying the
estimated cost per load times the number of
loads harvested at each lake, was: $1,384 at
Cluff Pond, $516 at Luna Lake, $577 at
Parker Canyon Lake, $600 at Pena Blanca
Lake, and $471 at Rainbow Lake.
Angler Use Survey
The majority of anglers at all reservoirs
(76%), and at each lake surveyed, thought
that aquatic vegetation hindered them from
fishing (Figure 17a). Half of the anglers
Year
1980 1985 1990 1995 2000 2005
Cost (Dollars)
0
20000
40000
60000
80000
100000
Monetary cost
Tons harvested
500
1000
1500
2000
2500
3000
3500
4000
Tons harvested
$1,265,038 expended from 1982 through 2006
Figure 15. Total cost (bars) of the Aquatic Weed
Harvesting Program and tons (wet weight, solid black
line) harvested per year from 1982 through 2006.
Table 4. Numbers of fish per 1 m3 sample of harvested
aquatic weeds at five Arizona reservoirs during 2005 (Pena
Blanca Lake) and 2006 (all other reservoirs).
Lake
Fish species
Cluff
Pond Luna
Parker
Canyon
Pena
Blanca Rainbow
Ameiurus melas 0 0 0 0 4
Lepomis macrochirus 99 0 0 8 1
Lepomis spp. 0 0 8 0 0
Micropterus salmoides 0 0 0 0 2
Pimephales promelas 0 5 1 0 0
Pomoxis nigromaculatus 1 0 0 0 0
Unknown 0 0 1 10 0
Total 100 5 9 18 7
Cluff Pond Luna Parker Cyn Rainbow Pena Blanca
Estimated
# fish/load
0
200
400
600
800
1000
Lake
Cluff Pond Luna Parker Cyn Rainbow Pena Blanca
Estimated
fish cost ($)/load
0
40
80
120
160
B
C
Cluff Pond Luna Parker Cyn Rainbow Pena Blanca
Estimated
# fish/m3
0
20
40
60
80
100
120
140
A
Figure 16. Estimated mean, and standard error, of
(A) number of fish incidentally harvested per m3,
(B) number of fish incidentally harvested per load,
and (C) cost of fish incidentally harvested per load
from five Arizona Reservoirs in 2005 (Pena Blanca
Lake) and 2006 (all other reservoirs shown). An
Aquarius Systems H-620 aquatic weed harvester
was used on Luna Lake and can hold 23.5 m3 of
plant material per load, whereas an Aquarius
Systems HM-220 aquatic weed harvester was used
on the other reservoirs and can hold 7.36 m3 of
plant material per load.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 3
Percent
0
20
40
60
80
100
120
Greatly Hinders
Moderately Hinders
No Effect
Moderately Improves
Greatly Improves
Does aquatic vegetation affect your fishing experience at this lake?
Percent
0
20
40
60
80
100
120
No
Yes
Does aquatic vegetation ever prevent you from fishing at this lake?
Lake
Arivaca
Cluff Pond
Luna
Nelson
Parker Canyon
Pena Blanca
Rainbow
All Lakes
Percent
0
20
40
60
80
100
120
Strongly Disagree
Moderately Disagree
No Opinion
Moderately Agree
Strongly Agree
Aquatic vegetation should be controlled.
36 28 39 21 53 39 165 394
A
B
C
36 26 39 22 55 38 159 388
33 24 31 24 51 37 165 378
Figure 17. Percent of anglers, with 95% confidence intervals, that gave each response to question 1 (A: Does
vegetation affect you fishing experience at this lake?), question 2 (B: Does vegetation ever prevent you from
fishing at this lake?), and statement 3 (C: Aquatic vegetation should be controlled.). Numbers above each group
of bars represents the number of anglers that responded at each lake.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 4
surveyed indicated that aquatic vegetation
prevented them from fishing at the survey
lake at least once, whereas the other half
indicated that aquatic vegetation never
prevented them from fishing at the lake
(Figure 17b). At Cluff Pond #3, Luna Lake
and Pena Blanca Lake most anglers
indicated that aquatic vegetation prevented
them from fishing at the lake, whereas at
Rainbow and Arivaca lakes most anglers
indicated that aquatic vegetation never
prevented them from fishing at the lake.
Parker Canyon Lake had approximately
equal proportions of ‘Yes’ and ‘No’
respondents. The majority of anglers
surveyed at all reservoirs combined (81.8%),
and at each lake surveyed, thought that
aquatic vegetation should be controlled
(Figure 17c). However, only at Parker
Canyon Lake and Rainbow Lake did
angler’s estimates of the percent of the lake
that was inaccessible decrease following or
during harvesting compared to the period
before harvesting (Figure 18). At Luna
Lake, angler assessment of percent of lake
that was inaccessible did not change from
before to after harvesting, and insufficient
data were collected at Cluff Pond and Pena
Blanca Lake to determine if angler
assessments of percent of the lake that was
inaccessible decreased after harvesting.
The number of days that an angler fished
that reservoir each year was correlated with
the responses to each of the questions (Table
5). The more days per year anglers fished,
the more likely they were to respond that
aquatic vegetation hindered their fishing
experience, or prevented them from fishing,
and the more likely they were to respond
that aquatic vegetation should be controlled.
In addition, as the number of days fished
increased, so did the angler’s assessment of
how much of the lake was inaccessible
because of aquatic vegetation coverage.
0 2 4 6 8 10 12
0
20
40
60
80
100
Pena Blanca
Lake
0 2 4 6 8 10 12
0
20
40
60
80
100
Cluff Pond #3
Month
0 2 4 6 8 10 12
0
20
40
60
80
100
0 2 4 6 8 10 12
0
20
40
60
80
100
Luna Lake
Parker Canyon
Lake
0 2 4 6 8 10 12
Percent Inaccessible
0
20
40
60
80
100
Rainbow Lake
Figure 18. Angler assessments of the percentage,
with 95% confidence intervals, of the lake that was
inaccessible due to aquatic plants (i.e., estimate of
percent aquatic plant coverage).
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 5
Anglers that thought that aquatic vegetation
hindered their fishing or prevented them
from fishing tended to think that aquatic
vegetation should be controlled.
DISCUSSION
STATEWIDE AQUATIC PLANT
SURVEY
Our data supports Santamaria (2002), that
aquatic vascular plants generally have broad
geographic ranges. Although many of the
taxa sampled had broad distributions, it is
important to note that elevation was a
significant predictor of occurrence for
several taxa. Two-leaf elodea, northern
watermilfoil, water knotweed, and water
buttercup were more likely to be found at
higher than at lower elevations, whereas
cattails and spiny naiad were more likely to
be found at low elevation reservoirs.
Eurasian watermilfoil, coontail, small
pondweed, creeping spikerush, and hard-stem
bulrush occurred at broader ranges of
elevations and so elevation was not a
significant predictor of occurrence for these
species. Eurasian watermilfoil presence at
reservoirs below 1,000 meters of elevation
displays its ability to be an invasive at lower
elevations where northern watermilfoil was
not found. Monitoring of reservoirs where
Eurasian watermilfoil is present will help us
better understand its invasive ability and
probability of spread in Arizona.
Average depth was also a significant
predictor of occurrence for two species.
Water knotweed and sago pondweed may be
light-limited and so were more likely to be
found in reservoirs that were shallow than
those that were deep; average depth was not
a significant predictor of occurrence for
other species examined. Average surface
area was not a significant predictor of
occurrence for any of the species examined,
but the number of species increased with
increasing reservoir surface area, in support
of island biogeography theory.
In most reservoirs, a single species did not
form a continuous monoculture. Rather, our
data indicate that most reservoirs had high
densities of several taxa. The most common
aquatic plant assemblage in Arizona
reservoirs was comprised of two-leaf elodea,
water knotweed, and coontail. Nineteen
pairs of species tended to co-occur but there
were four instances of negative co-occurrence.
Negative species associations
might result from competition, or other
factors such as environmental requirements,
dispersal vectors, or stochastic processes;
experimental studies would be needed to
confirm competition. Cattail and creeping
spikerush were negatively associated with
one another, and given that both are
emergent species, it makes sense that they
may compete. Eurasian watermilfoil was
negatively associated with muskgrass,
suggesting that it may compete with this
species or environmental requirements of
the two species may be different, or there
may be other environmental conditions such
as water quality and nutrient composition in
specific reservoirs that may be causing this
negative association.
Table 5. Correlations among questions and days fished at
the reservoir; r = Pearson’s correlation coefficient, P =
significance level, n = sample size.
Question 1 Question 2 Question 3 Statement 4
Question 2 r 0.520
P <0.001
n 374
Question 3 r -.481 -0.483
P <0.001 <0.001
n 384 367
Statement 4 r -0.379 -0.357 0.227
P <0.001 <0.001 <0.001
n 390 372 385
Days fished r -0.133 -0.321 0.182 0.163
at reservoir P 0.012 <0.001 0.001 0.002
n 361 345 354 360
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 6
Although results of other studies (Madsen et
al. 1991, Boylen et al. 1999) have indicated
that a decline in native vegetation can occur
under dense Eurasian watermilfoil canopies,
our data for the most part do not support this
contention. Eurasian watermilfoil had
greater percent composition than the
cumulative percent composition of native
plants at only three of the nine reservoirs
where it occurred, and at only two
reservoirs, Parker Canyon Lake and
Goldwater Lake, was Eurasian watermilfoil
the most dominant aquatic plant. Madsen
(1998) found that reservoirs with more than
50% Eurasian watermilfoil dominance had
less than 60% cumulative native plant
coverage. Our data do not support this
contention; at five of the nine reservoirs
with Eurasian watermilfoil in our study,
Eurasian watermilfoil had compositions
greater than 50%, but of these five, four
were shallow reservoirs for which we
estimated percent coverage and only one had
less than 60% cumulative native plant
coverage. At the five reservoirs where
native aquatic plants had higher percentage
composition than Eurasian watermilfoil, it
may be that time is needed for this
macrophyte to increase coverage, native
species in Arizona may out-compete this
nonnative macrophyte, or that
environmental requirements and dispersal
vectors may be limiting this species success
in Arizona. Nichols and Shaw (1986)
reported that harvesting can encourage the
spread of nuisance species because many
species are able to propagate rapidly from
plant fragments. It is likely that Eurasian
watermilfoil has spread throughout Arizona
reservoirs as a result of the Department’s
harvesting program, because nine of the ten
reservoirs with Eurasian watermilfoil
present were reservoirs that have been
harvested.
Other studies have concluded that Eurasian
watermilfoil could out-compete northern
watermilfoil (Nichols 1994, but see Valley
and Newman 1998 for an opposite
conclusion) and spiny naiad (Agami and
Waisel 1985). We did not detect a
significant negative association in
occurrence between the Eurasian
watermilfoil and northern watermilfoil
(Table 3), but at the one reservoir where the
two species co-occurred (Goldwater Lake),
Eurasian watermilfoil had a greater percent
composition (87%) than northern
watermilfoil (70%), lending some indirect
support to the hypothesis that Eurasian
watermilfoil is the superior competitor.
Similarly, we did not detect a significant
negative association between Eurasian
watermilfoil and spiny naiad, but at Alamo
Lake, the only reservoir where both species
were present, spiny naiad comprised 12% of
the species composition and Eurasian
watermilfoil was 41%, lending indirect
support to the findings of Agami and Waisel
(1985).
We think that reservoir water levels,
bathymetry, and substrate might explain the
lack of aquatic macrophytes at four
reservoirs. Unlike other reservoirs we
examined, Lake Pleasant experiences large
seasonal fluctuations in water level, which
likely resulted in the absence of aquatic
vegetation at this reservoir; U. S. Bureau of
Reclamation pumps water into and stores
water in the reservoir during winter and
pumps water out into the Central Arizona
Project canal during summer. Knoll Lake
and Woods Canyon Lake were deep and had
steep rocky sides and rocky substrates, so
areas suitable for rooted aquatic vegetation
were restricted to the few shallow stream
inflow areas with fine substrates, which, for
some unknown reason, were still absent of
aquatic vegetation. Cataract Lake was not
as deep as the other three reservoirs, but it
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 7
still had mostly steep rocky sides and its
bottom substrate was the same as the deeper
reservoirs.
Filamentous algae and the native aquatic
plants coontail, northern watermilfoil, sago
pondweed, and spiny naiad, and the
nonnative Eurasian watermilfoil had
relatively high prevalence statewide (> 21%)
and each had percent frequency of
occurrence (an estimate of percent cover in
our shallow reservoirs) in excess of 24%.
These six taxa are, therefore, good targets
for management. Muskgrass had a high
prevalence but low percent frequency of
occurrence within reservoirs, therefore, it is
probably less of a management concern.
Another species, curly-leafed pondweed,
was listed as a problem by eight states
because of its invasive and competitive
abilities with other native aquatic plants
(Bartodziej and Ludlow1997). This species
may become problematic in Arizona, but, at
present is not of widespread concern. Curly-leafed
pondweed was only found at two
reservoirs; it was rare at Mittry Lake, but at
Granite Basin Lake it covered 64% of the
reservoir with a composition of 87%.
EVALUATION OF HARVESTING
PROGRAM
Aquatic Vegetation Coverage, Fish Kills,
and Water Chemistry
There were several climatic events that
affected the outcome of our monitoring of
harvested reservoirs. Lake levels were
affected by drought and precipitation. The
persistence of the drought, which began in
1996, caused water levels to decrease so low
in several targeted reservoirs (Arivaca Lake
and Nelson Reservoir) during 2006 that the
harvesters could not be launched and hence
the reservoirs could not be harvested. The
list of potential alternatives was so short that
two reservoirs that were harvested in the
previous year (Parker Canyon Lake and
Luna Lake) had to be chosen. The
reservoirs that were monitored during 2006
all experienced increases in water levels,
because of summer thunderstorms, during
the period when they were harvested. The
increase in water levels decreased the
efficiency of harvesting because the
harvesters can only cut to a depth of 1.5 m,
and the lake levels increased by 1-2 m. In
addition to affecting the efficiency of
harvesting, the increased flow into the
reservoir increased lake volume which may
have affected water chemistry.
Another factor may have affected study
results at Pena Blanca Lake. We consulted
with the harvester crew and selected control
and treatment coves in Pena Blanca Lake
and began monitoring in April 2005. After
the third sampling event, we found out that
aquatic weeds harvested were dumped
onshore-near shore at the back of our control
cove; the US Forest Service would not grant
permission to dump them on land because of
mercury concerns. Therefore, the increase
in plant matter may have affected water
quality in the control cove.
We detected a decrease in aquatic vegetation
cover following harvesting at most of the
reservoirs we monitored. However, changes
in water quality as a result of harvesting
were not evident. Either harvesting had
negligible effects on the variables we
measured, our measurements were not
sensitive enough to detect changes, or
environmental events affected our results.
We think that harvesting had negligible
effects on water quality in the reservoirs we
monitored. After examining the 2005 data,
we did not see any clear changes in water
chemistry variables as a result of harvesting.
We wanted to rule out the possibility that
mid-day readings were too variable to detect
changes, if there were harvest-related
changes. Therefore, during 2006, we
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 8
deployed sondes for 22 hours at monitored
reservoirs, but we still failed to detect
changes in pH, water temperature or
dissolved oxygen from before to after
harvesting. Reservoir level increased during
the harvesting period within each of the
reservoirs that we monitored during 2006,
which may have affected water quality, but
the effect should have been similar in
control and treatment areas. Even so, we did
not detect divergence in water quality values
between treatment and control locations.
Therefore, our data indicate that harvesting
did not have a detectable effect on water
quality variables measured. In other words,
we did not find evidence in support of two
of our hypotheses relative to water
chemistry; pH did not decrease and
dissolved oxygen did not increase following
harvesting. In addition, we did not detect a
decrease in the numbers of dead fish
observed from before to after harvesting, but
given that we did not detect a change in
dissolved oxygen concentrations, this is not
surprising.
Effects of macrophytes on dissolved oxygen,
pH, water temperature and chlorophyll
concentrations tend to be localized (Wetzel
1983, Carter et al. 1991); levels are high in
surface waters in macrophyte beds and low
near the bottom. We measured dissolved
oxygen, pH and water temperature at 1-m
depth, so we might have detected increases
in these variables if we had measured them
at the surface. Regardless, if a reservoir has
considerable unvegetated areas, it appears
that phytoplankton will have more of a lake-wide
effect on pH and dissolved oxygen
than will aquatic macrophytes (Carter et al.
1991).
Macrophytes are most likely to affect water
nutrient levels when they are senescing and
plant matter is decomposing (Landers 1982).
Harvesting aquatic vegetation has lowered
phosphorus levels in lakes under certain
conditions (Nichols 1991), but under most
conditions, harvesting does not result in
lower nutrient levels (Carpenter and Adams
1977). We did not detect changes in
phosphorus or nitrate concentrations
following harvesting. Most of our nutrient
measurements were made prior to the
senescence period (autumn); regardless we
did not see consistent increases in nutrients
in autumn among the four reservoirs
monitored nor were nutrient concentrations
greater in the control areas relative to the
treatment areas during autumn. Therefore,
we did not find evidence in support of our
third hypothesis relative to water chemistry
that nutrient levels after harvesting
decreased in treatment areas relative to
control areas.
Dissolved oxygen and nutrient
concentrations and pH may have been more
dependent upon phytoplankton, as effects of
phytoplankton on these variables are well
known (Wetzel 1983). However, we did not
find significant correlations between mid-day
chlorophyll a concentrations and
dissolved oxygen or pH at most of the lakes
monitored; Rainbow Lake was the
exception. We also did not detect any
divergence in phytoplankton at treatment
and control locations, as measured by
chlorophyll a concentrations, after
harvesting, which supports published reports
that mechanical control operations rarely
cause algal bloom formation or other major
changes in phytoplankton community
structure (Wile and Hitchin 1977; Wile
1978; Engel 1990).
Operational Cost of Harvesting
The harvesting program expends
approximately $50,600 per year to harvest
an average of six reservoirs. This annual
cost seems relatively small compared to the
$250,000 - $300,000 annually spent
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
2 9
mechanically harvesting approximately
1000 tons of aquatic weeds in Big Bear
Lake, California (Frieman et al. 2004). We
did not detect any consistent downward
trends from year to year in tons of aquatic
plants harvested, lending little support to the
hypothesis that yearly harvesting depletes
the nutrients in a lake and results in less
plant biomass in successive years.
Incidental Fish
Harvesting can remove fish and
invertebrates that are tangled in the
vegetation (Wile 1978, Haller et al. 1980,
Engel 1990). Our data indicate that
relatively few fish were removed by
harvesting aquatic plants. We found mostly
young-of-year (YOY) gamefish, and a few
small minnows in the samples of harvested
aquatic plants that we examined. Over the
course of harvesting a lake, several hundred
fish are likely removed, but because they are
YOY fish, the monetary value is not very
great. In addition, most species of fish
produce large amounts of young, and most
of those young die within the first year of
life. Therefore, from a population
perspective, an individual YOY fish is
expendable. Therefore, incidental fish
removal as a result of harvesting aquatic
vegetation is probably not of much concern
in the reservoirs that we monitored.
Angler Survey
Wilde et al. (1992) reported that 22 to 35%
of anglers supported aquatic vegetation
removal in Texas. In contrast, we found that
a super-majority (more than 75%) of anglers
surveyed indicated that aquatic vegetation
hindered their fishing experience, and
should be controlled. In addition, the more
days an angler fished per year the more they
thought that aquatic vegetation should be
controlled. Hence, anglers support the
Department’s efforts to control aquatic
vegetation in problem reservoirs throughout
the state. At two reservoirs, anglers
estimation of percent of the lake that was
inaccessible decreased immediately during
or following harvesting, suggesting that the
Department’s aquatic weed harvesting
program is increasing access for anglers for
approximately a month following
harvesting.
MANAGEMENT OPTIONS
Management of aquatic plants to improve
access in Arizona’s reservoirs should focus
on filamentous algae, the non-native species
Eurasian watermilfoil and curly-leafed
pondweed, and the native species coontail,
sago pondweed, spiny naiad, and northern
watermilfoil. These species can cover
extensive areas of reservoirs and form dense
stands that hinder various recreational
activities such as boating access, fishing,
and swimming. Management of these
species will inadvertently affect other plant
species in the assemblage, particularly those
that we found to co-occur with these species.
The occurrence of the non-native invasive
species Eurasian watermilfoil, curly-leafed
pondweed, and others that have been
recorded in the state during the past (e.g.,
giant salvinia, Salvinia molesta, and
Hydrilla, Hydrilla verticillata) need to be
monitored so that action can be taken to
prevent their spread.
COST AND BENEFITS OF HARVESTING
Costs:
• Aquatic plant harvesting has likely
resulted in the spread of the invasive
Eurasian watermilfoil to reservoirs
throughout Arizona.
• The aquatic plant harvesting program
expends approximately $50,600 per
year.
• Relatively few sport fish are
removed by aquatic plant harvesting,
and those that are removed tend to be
expendable YOY fish.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 0
Benefits:
• Harvesting immediately decreases
aquatic vegetation cover and hence
improves angler access.
• The majority of anglers are in favor
of controlling aquatic vegetation.
• Harvesting is less contentious to the
general public than using chemicals
to control aquatic weeds.
Aquatic plant harvesting apparently does not
change water chemistry enough to reduce
fish kills or extend the trout stocking season.
Based on the above mentioned costs and
benefits, harvesting aquatic weeds is
probably a worthwhile venture for the
Department, especially because our angling
customers want aquatic vegetation to be
controlled. However, we suggest that more
effective decontamination procedures for the
harvesting machinery be implemented to
limit the spread of invasive species. In
addition, other techniques to control aquatic
vegetation such as biological (grass carp) or
chemical control should be considered on a
case-by-case basis.
LITERATURE CITED
Agami, M., and Y. Waisel. 1985. Inter-relationships
between Najas marina
L. and three other species of aquatic
macrophytes. Hydrobiologia 126:
169-173.
APHA (American Public Health
Association), American Water
Works Association, and Water
Environment Federation. 2005.
Standard methods for the
examination of water and
wastewater, 21st edition. American
Public Health Association,
Washington, D.C.
American Fisheries Society. 1992.
Investigation and valuation of fish
kills. American Fisheries Society
Special Publication 24, Bethesda,
Maryland, USA
Bartodziej, W., and J. Ludlow. 1997.
Aquatic vegetation monitoring by
natural resource agencies in the
United States. Journal of Lake and
Reservoir Management 13(2): 109-
117.
Booms, T. L. 1999. Vertebrates removed
by mechanical weed harvesting in
Lake Keesus, Wisconsin. Journal of
Aquatic Plant Management 37:34-
36.
Boylen, C.W., Eichler, L.W., and J.D.
Madsen. 1999. Loss of native
aquatic plant species in a community
dominated by Eurasian watermilfoil.
Hydrobiologia 415: 207-211.
Carpenter, S. R., and M. S. Adams. 1977.
The macrophyte tissue nutrient pool
of a hardwater eutrophic lake:
implications for macrophyte
harvesting. Aquatic Botany 3:239-
255.
Carpenter, S. R., and D. M. Lodge. 1986.
Effects of submersed macrophytes
on ecosystem processes. Aquatic
Botany 26:341-370.
Carter, V., N. B. Rybicki, R. Hammerschlag.
1991. Effects of submersed
macrophytes on dissolved oxygen,
pH, and temperature under different
conditions of wind, tide, and bed
structure. Journal of Freshwater
Ecology 6:121-133.
Elzinga, C. L., D. W. Salzer, J. W.
Willoughby, and J. P. Gibbs. 2001.
Monitoring plant and animal
populations. Blackwell Science,
Inc., Malden, MA.
Engel, S. 1990. Ecological impacts of
harvesting macrophytes in Halverson
Lake, Wisconsin. Journal of Aquatic
Plant Management 28:41-45.
Frieman, G. M., G. V. Lehman, J. D. Quinn,
and M. E. Wittmann. 2004. A cost
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 1
and environmental analysis of
aquatic plant management in
California. Master’s Thesis.
University of California, Santa
Barbara.
Gibbons, M.V., M.G. Rosendranz, H.L.
Gibbons, Jr., and M.D. Sytsma.
1999. Guide for developing
integrated aquatic vegetation
management plans in Oregon.
Center for Lakes and Reservoirs,
Portland State University, Portland,
Oregon.
Haller, W. T., J. V. Shireman, and F. F.
DuRant. 1980. Fish harvest
resulting from mechanical control of
hydrilla. Transactions of the
American Fisheries Society 109:517-
520.
Landers, D. H. 1982. Effects of naturally
senescing aquatic macrophytes on
nutrient chemistry and chlorophyll a
of surrounding waters. Limnology
and Oceanography 27: 428-439.
Lembi, C. A. 2003. Aquatic plant
management. WS-21-W. Purdue
University Cooperative Extension
Service, Purdue, Indiana.
Madsen, J.D., J.W. Sutherland, J.A.
Bloomfield, L.W. Eichler, and C.W.
Boylen. 1991. The decline of native
vegetation under dense Eurasian
watermilfoil canopies. Journal of
Aquatic Plant Management 29: 94-
99.
Madsen, J.D. 1998. Predicting invasion
success of Eurasian watermilfoil.
Journal of Aquatic Plant
Management 36: 28-32.
Madsen, J. D. 1999. Point intercept and
line intercept methods for aquatic
plant management. Aquatic Plant
Control Technical Notes Collection
TN APCRP-MI-02. U.S. Army
Engineer Research and Development
Center, Vicksburg, MS.
Manly, F. J., L. L. McDonald, D. L.
Thomas, T. L. McDonald, and W. P.
Erickson. 2002. Resource selection
by animals: statistical design and
analysis for field studies. Second
edition. Kluweer, Dordreccht, The
Netherlands.
Mikol, G. F. 1985. Effects of harvesting
aquatic vegetation and juvenile fish
populations at Saratoga Lake, New
York. Journal of Aquatic Plant
Management 23:59-63.
Nichols, S. A. 1991. The interaction
between biology and the
management of aquatic macrophytes.
Aquatic Botany 41: 225-252.
Nichols, S.A. 1994. Evaluation of
invasions and declines of submersed
macrophytes for the upper Great
Lakes region. Lake and Reservoir
Management 10: 29-33.
Nichols, S. A., and B. H. Shaw. 1986.
Ecological life histories of three
aquatic nuisance plants,
Myriophyllum spicatum,
Potomogeton crispus, and, Elodea
canadensis. Hydrobiologia 131:3-
21.
Santamaria, L. 2002. Why are most aquatic
plants widely distributed? Dispersal,
clonal growth and small-scale
heterogeneity in a stressful
environment. Acta Oecologica 23:
137-154.
SPSS. 2003. SPSS/PC+, version 12.0.1
SPSS, Chicago.
Titus, J.E. 1993. Submersed macrophyte
vegetation and distribution within
lakes: line transect sampling. Lake
and Reservoir Management. 7: 155-
164.
TOPO! 2002. National Geographic TOPO!,
version 2.7.7 National Geographic
Maps, San Francisco.
Valley, R.D. and R.M. Newman. 1998.
Competitive interactions between
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 2
Eurasian watermilfoil and northern
watermilfoil in experimental tanks.
Journal of Aquatic Plant
Management 36: 121-126.
Wetzel, R. G. 1983. Limnology, second
edition. Saunders College
Publishing, New York.
Wilde, G. R., R. K. Riechers, and J.
Johnson. 1992. Angler attitudes
toward control of freshwater
vegetation. Journal of Aquatic Plant
Management 30:77-79.
Wile, I. 1978. Environmental effects of
mechanical harvesting. Journal of
Aquatic Plant Management 16:14-
20.
Wile, I., and G. Hitchin. 1977. An
assessment of the practical and
environmental implications of
mechanical harvesting of aquatic
vegetation in Southern Chemung
Lake. Ontario Ministry of the
Environment and Ministry of Natural
Resources.
Wile, I., G. Hitchin, and G. Beggs. 1979.
The impact of mechanical harvesting
on Chemung Lake. Pages 145-159
in J. E. Breck, R. T. Prentki, and O.
L. Loucks (editors). Aquatic plants,
lake management, and ecosystem
consequences of lake harvesting.
Institute of Environmental Studies,
University of Wisconsin, Madison.
Zar, J.H. 1984. Biostatistical Analysis, 2nd
edition. Prentice-Hall Inc., Englewood
Cliffs, New Jersey.
For more detailed and technical
presentations of methods, results and
discussion of specific sections of this
Technical Guidance Bulletin, the authors
refer you to the following citations, which
can be obtained by contacting:
Anthony T. Robinson
Research Branch
Arizona Game and Fish Department
2221 W. Greenway Road
Phoenix, AZ 85023
(602)-789-3376
trobinson@gf.state.az.us
Fulmer, J.E. and A.T. Robinson. 2008 In
Press. Aquatic plant species distributions
and associations in Arizona’s reservoirs.
Journal of Aquatic Plant Management.
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 3
APPENDIX
Data Summary Tables
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 4
Table A1. Aquatic plant taxa found at each surveyed lake in Arizona, 2004-2006, and percent
composition and percent frequency of each taxa. Definitions are as in Table 2.
Lake
No. points
surveyed
No.
points
with
plants Taxa
No.
points
with
taxa
%
Composition
%
Frequency
Alamo Lake 100 49 Cyperus spp. 25 51.0
Lemna minima 1 2.0
Myriophyllum spicatum 20 40.8
Najas marina 6 12.2
Antelope Lake 100 88 Chara spp. 10 11.4 10.0
Filamentous algae 10 11.4 10.0
Myriophyllum sibiricum 76 86.4 76.0
Potamogeton pusillus 60 68.2 60.0
Rannunculus longirostris 3 3.4 3.0
Apache Lake 100 36 Cyperus spp. 1 2.8
Encrusting algae 2 5.6
Phragmites communis 13 36.1
Polygonum spp. 1 2.8
Typha spp. 25 69.4
Arivaca Lake 100 88 Ceratophyllum demersum 84 95.5
Clinging algae 29 33.0
Filamentous algae 19 21.6
Planktonic alga bloom 2 2.3
Myriophyllum spicatum 34 38.6
Becker Lake 100 56 Ceratophyllum demersum 3 5.4 3.0
Chara spp. 17 30.4 17.0
Filamentous algae 14 25.0 14.0
Myriophyllum sibiricum 37 66.1 37.0
Polygonum amphibium 5 8.9 5.0
Schoenoplectnus acutus 4 7.1 4.0
Stuckenia pectinata 21 37.5 21.0
Big Springs Pond 20 18 Chara spp. 10 55.6 50.0
Eleocharis palustris 1 5.6 5.0
Filamentous algae 12 66.7 60.0
Rorippa nasturtium-aquaticum 2 11.1 10.0
Unknown wetland plant 1 5.6 5.0
Cluff Pond #3 100 53 Filamentous algae 15 28.3 15.0
Myriophyllum sibiricum 53 100.0 53.0
Typha spp. 1 1.9 1.0
Concho Lake 100 93 Ceratophyllum demersum 53 57.0 53.0
Eleocharis palustris 1 1.1 1.0
Elodea bifoliata 11 11.8 11.0
Filamentous algae 1 1.1 1.0
Myriophyllum spicatum 3 3.2 3.0
Polygonum amphibium 1 1.1 1.0
Stuckenia pectinata 85 91.4 85.0
Crescent Lake 100 79 Elodea bifoliata 65 82.3 65.0
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 5
Lake
No. points
surveyed
No.
points
with
plants Taxa
No.
points
with
taxa
%
Composition
%
Frequency
Filamentous algae 38 48.1 38.0
Myriophyllum sibiricum 43 54.4 43.0
Stuckenia pectinata 2 2.5 2.0
Dankworth Pond 100 79 Chara spp. 1 1.3 1.0
Filamentous algae 54 68.4 54.0
Najas marina 70 88.6 70.0
Nitella spp. 17 21.5 17.0
Stuckenia pectinata 22 27.8 22.0
Typha spp. 12 15.2 12.0
Unknown macrophyte 1 32 40.5 32.0
Unknown macrophyte 2 1 1.3 1.0
Unknown macrophyte 3 1 1.3 1.0
Ganado Lake 100 87 Chara spp. 2 2.3 2.0
Eleocharis palustris 2 2.3 2.0
Elodea bifoliata 62 71.3 62.0
Filamentous algae 5 5.7 5.0
Potamogeton pusillus 6 6.9 6.0
Stuckenia pectinata 62 71.3 62.0
Goldwater Lake Myriophyllum sibiricum 16 69.6 16.0
Myriophyllum spicatum 20 87.0 20.0
Granite Basin
Lake 100 74 Eleocharis palustris 2 2.7 2.0
Filamentous algae 3 4.1 3.0
Lemna minima 4 5.4 4.0
Potamogeton crispus 64 86.5 64.0
Potamogeton pusillus 1 1.4 1.0
Schoenoplectnus acutus 1 1.4 1.0
Typha spp. 11 14.9 11.0
Lower Lake Mary 100 50 Ceratophyllum demersum 33 66.0 33.0
Chara spp. 1 2.0 1.0
Elodea bifoliata 4 8.0 4.0
Filamentous algae 1 2.0 1.0
Polygonum amphibium 15 30.0 15.0
Luna Lake 100 80 Carex spp. 1 1.3 1.0
Ceratophyllum demersum 33 41.3 33.0
Eleocharis palustris 5 6.3 5.0
Elodea bifoliata 50 62.5 50.0
Filamentous algae 11 13.8 11.0
Glyceria grandis 2 2.5 2.0
Myriophyllum spicatum 62 77.5 62.0
Polygonum amphibium 19 23.8 19.0
Rannunculus longirostris 9 11.3 9.0
Schoenoplectnus acutus 1 1.3 1.0
Stuckenia pectinata 25 31.3 25.0
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 6
Lake
No. points
surveyed
No.
points
with
plants Taxa
No.
points
with
taxa
%
Composition
%
Frequency
Lynx Lake 100 45 Cyperus esculentus 1 2.2
Echinochloa crus-galli 3 6.7
Filamentous algae 1 2.2
Myriophyllum sibiricum 34 75.6
Myriophyllum spicatum 5 11.1
Polygonum argyrocoleon 1 2.2
Potamogeton pusillus 2 4.4
Typha spp. 2 4.4
Marshall Lake 99 85 Ceratophyllum demersum 25 29.4 25.3
Chara spp. 6 7.1 6.1
Cyperus spp. 1 1.2 1.0
Eleocharis palustris 1 1.2 1.0
Elodea bifoliata 34 40.0 34.3
Filamentous algae 6 7.1 6.1
Myriophyllum sibiricum 1 1.2 1.0
Polygonum amphibium 13 15.3 13.1
Rannunculus longirostris 3 3.5 3.0
Schoenoplectnus acutus 29 34.1 29.3
Stuckenia pectinata 58 68.2 58.6
Martinez Lake 100 70 Chara spp. 2 2.9 2.0
Filamentous algae 2 2.9 2.0
Najas marina 68 97.1 68.0
Phragmites communis 3 4.3 3.0
Schoenoplectnus acutus 4 5.7 4.0
Stuckenia pectinata 4 5.7 4.0
Typha spp. 5 7.1 5.0
Mittry Lake 100 45 Ceratophyllum demersum 7 15.6 7.0
Chara spp. 2 4.4 2.0
Filamentous algae 1 2.2 1.0
Najas marina 41 91.1 41.0
Phragmites communis 4 8.9 4.0
Potamogeton pusillus 2 4.4 2.0
Schoenoplectnus acutus 4 8.9 4.0
Stuckenia pectinata 7 15.6 7.0
Typha spp. 5 11.1 5.0
Nelson Reservoir 100 100 Ceratophyllum demersum 32 32.0 32.0
Chara spp. 14 14.0 14.0
Elodea bifoliata 5 5.0 5.0
Filamentous algae 99 99.0 99.0
Myriophyllum sibiricum 41 41.0 41.0
Polygonum amphibium 2 2.0 2.0
Rannunculus longirostris 4 4.0 4.0
Stuckenia pectinata 37 37.0 37.0
Typha spp. 1 1.0 1.0
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 7
Lake
No. points
surveyed
No.
points
with
plants Taxa
No.
points
with
taxa
%
Composition
%
Frequency
Unknown Nelson macrophyte1 4 4.0 4.0
Parker Canyon
Lake 100 94 Cyperus odoratus 2 2.1
Filamentous algae 7 7.4
Myriophyllum spicatum 93 98.9
Nitella spp. 2 2.1
Schoenoplectnus acutus 1 1.1
Pasture Canyon
Lake 100 100 Ceratophyllum demersum 48 48.0 48.0
Chara spp. 45 45.0 45.0
Eleocharis palustris 1 1.0 1.0
Filamentous algae 4 4.0 4.0
Myriophyllum sibiricum 58 58.0 58.0
Schoenoplectnus acutus 14 14.0 14.0
Stuckenia pectinata 23 23.0 23.0
Patagonia Lake 100 22 Algae 1 Patagonia 2 9.1
Algae 2 Patagonia 2 9.1
Algae 3 Patagonia 5 22.7
Ceratophyllum demersum 1 4.5
Chara spp. 1 4.5
Najas marina 1 4.5
Typha spp. 16 72.7
Pena Blanca
Lake 100 99 Ceratophyllum demersum 98 99.0
Filamentous algae 36 36.4
Spirodela polyrhiza 4 4.0
Typha spp. 14 14.1
Quigley Pond 100 31 Chara spp. 3 9.7 3.0
Filamentous algae 26 83.9 26.0
Typha spp. 10 32.3 10.0
Rainbow Lake 100 99 Ceratophyllum demersum 77 77.8 77.0
Chara spp. 1 1.0 1.0
Eleocharis parishii 1 1.0 1.0
Elodea bifoliata 44 44.4 44.0
Filamentous algae 41 41.4 41.0
Myriophyllum spicatum 59 59.6 59.0
Polygonum amphibium 6 6.1 6.0
Pontederia spp. 7 7.1 7.0
Potamogeton pusillus 12 12.1 12.0
Schoenoplectnus acutus 1 1.0 1.0
Riggs Flat Lake 100 63 Chara spp. 39 61.9 39.0
Eleocharis spp. 1 1.6 1.0
Filamentous algae 42 66.7 42.0
Myriophyllum spicatum 54 85.7 54.0
Potamogeton pusillus 39 61.9 39.0
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 8
Lake
No. points
surveyed
No.
points
with
plants Taxa
No.
points
with
taxa
%
Composition
%
Frequency
Rannunculus longirostris 3 4.8 3.0
Roper Lake 100 51 Chara spp. 31 60.8 31.0
Filamentous algae 8 15.7 8.0
Najas marina 39 76.5 39.0
Potamogeton filiformis 1 2.0 1.0
Stuckenia pectinata 7 13.7 7.0
Typha spp. 3 5.9 3.0
Rose Canyon
Lake 100 4 Schoenoplectnus acutus 1 25.0 1.0
Typha spp. 3 75.0 3.0
Stoneman Lake 100 100 Ceratophyllum demersum 72 72.0 72.0
Chara spp. 87 87.0 87.0
Elodea bifoliata 27 27.0 27.0
Filamentous algae 9 9.0 9.0
Myriophyllum sibiricum 2 2.0 2.0
Polygonum amphibium 19 19.0 19.0
Schoenoplectnus acutus 18 18.0 18.0
Sparganium spp. 1 1.0 1.0
Stuckenia pectinata 96 96.0 96.0
Topock Marsh 180 137 Chara spp. 36 26.3 20.0
Najas marina 112 81.8 62.2
Schoenoplectnus acutus 12 8.8 6.7
Stuckenia pectinata 60 43.8 33.3
Typha spp. 7 5.1 3.9
Tsalie Lake 100 70 Ceratophyllum demersum 62 88.6 62.0
Chara spp. 3 4.3 3.0
Filamentous algae 34 48.6 34.0
Myriophyllum sibiricum 35 50.0 35.0
Potamogeton pusillus 2 2.9 2.0
Willow Creek
Lake 100 84 Azolla filiculoides 6 7.1 6.0
Ceratophyllum demersum 1 1.2 1.0
Chara spp. 4 4.8 4.0
Crypsis schoenoides 2 2.4 2.0
Eleocharis palustris 4 4.8 4.0
Eleocharis parishii 1 1.2 1.0
Elodea bifoliata 39 46.4 39.0
Filamentous algae 68 81.0 68.0
Lemna minima 1 1.2 1.0
Myriophyllum sibiricum 4 4.8 4.0
Najas guadalupensis 3 3.6 3.0
Najas marina 1 1.2 1.0
Polygonum amphibium 7 8.3 7.0
Potamogeton foliosus 1 1.2 1.0
Robinson et al. 2007---Aquatic Plant Surveys and Evaluation of Aquatic Plant Harvesting
3 9
Lake
No. points
surveyed
No.
points
with
plants Taxa
No.
points
with
taxa
%
Composition
%
Frequency
Potamogeton pusillus 13 15.5 13.0
Stuckenia pectinata 58 69.0 58.0
Veronica anagallis-aquatica 1 1.2 1.0
Woodland Lake 100 100 Ceratophyllum demersum 35 35.0 35.0
Elodea bifoliata 80 80.0 80.0
Filamentous algae 46 46.0 46.0
Myriophyllum sibiricum 81 81.0 81.0
Polygonum amphibium 2 2.0 2.0
Schoenoplectnus acutus 1 1.0 1.0
Stuckenia pectinata 42 42.0 42.0
Typha spp. 1 1.0 1.0
Table A2. Results of forward-stepwise logistic regressions showing coefficients with standard
errors, Wald statistics, probabilities, and –2 times log-likelihood (-2 LL) of the included
variables in the final models. Elevation (m), average depth (m), and average area (ha) were input
into each model. Models for coontail, creeping spikerush, Eurasian watermilfoil, small
pondweed, and hard-stem bulrush were not significant and are not shown.
Taxa Variable B SE Wald P -2 LL
Two-leaf elodea Constant
Elevation (m)
-5.265
0.002
2.164
0.001
5.919
5.079
0.015
0.024
34.859
Northern watermilfoil Constant
Elevation (m)
-2.273
0.001
1.204
0.001
5.111
4.272
0.024
0.039
43.251
Spiny naiad Constant
Elevation (m)
2.007
-0.003
1.084
0.001
3.431
9.278
0.064
0.002
22.253
Water knotweed Constant
Elevation (m)
Depth (m)
-1.849
0.001
-0.224
1.375
0.001
0.104
1.809
4.403
4.670
0.179
0.036
0.031
32.603
Water buttercup Constant
Elevation (m)
-12.679
0.005
5.630
0.003
5.072
4.235
0.024
0.040
18.551
Sago pondweed Constant
Depth (m)
0.958
-0.188
0.548
0.082
3.060
5.261
0.080
0.022
39.794
Cattail Constant
Elevation (m)
2.627
-0.002
1.150
0.001
6.672
5.218
0.022
0.010
41.592