2011 SOUTHEASTERN NATURALIST 10(1):109–120
Influence of Abiotic Factors on Activity in a Larval Stream
Salamander Assemblage
Grant M. Connette1,2,*, Steven J. Price1,3, and Michael E. Dorcas1
Abstract - Larval stream salamanders are the numerically dominant predators in many
headwater stream systems. Nonetheless, little is known about their activity patterns or the
extent to which their movements are influenced by prevailing environmental conditions.
In this study, we used capture rates from passive trapping as an index of activity level
and sought to identify the environmental variables most responsible for fluctuations in
larval stream salamander activity. Over the course of two months, we captured stream
salamanders in aquatic funnel traps during both day- and night-trapping sessions at a
first-order stream in the North Carolina Piedmont. Using an information-theoretic approach,
we constructed models to elucidate the effects of (1) water temperature, (2) cloud
cover, (3) days since last rainfall, and (4) time of day on larval salamander activity. We
found that the model incorporating time of day and cloud cover was the best predictor of
larval salamander activity. In our study, larval salamander activity was highest at night
and also demonstrated a weak positive correlation with increasing cloud cover. Using
model-averaging, we further determined that our time of day and cloud cover variables
demonstrated a significant correlation with observed activity levels. This pattern of peak
activity under low light conditions could be a behavioral adaptation that limits predation
risk for larval salamanders.
Introduction
Understanding the activity patterns of a species is a critical step towards
understanding how that species interacts with its environment. In many cases,
the daily activity pattern of an individual may be dictated by competing requirements
for resource acquisition and predator avoidance (Werner and Anholt 1993,
Yurewicz 2004). For many salamanders, this has been shown to result in shifts in
active periods or patterns of microhabitat use during times of perceived predation
risk (Barr and Babbitt 2007, Holomuzki 1986, Madison et al. 1999, Maerz
et al. 2001). Furthermore, the activity patterns of adult stream salamanders have
been shown to correspond with daily peaks in prey abundance (Holomuzki 1980).
Stream salamander activity can also be broadly shaped by cyclical factors such
as season or reproductive cycle (Orser and Shure 1975). Over the short term,
however, much of the observed variability in the activity levels of adult stream
salamanders may be correlated with changes in environmental conditions such as
temperature, rainfall, or substrate moisture (Barbour et al. 1969, Hairston 1949,
Keen 1984, Orser and Shure 1975).
1Department of Biology, Davidson College, Davidson, NC 28035-7118. 2Division of
Biological Sciences, University of Missouri, Columbia, MO 65211-7400. 3Department
of Biology, Wake Forest University, Winston-Salem, NC 27109. *Corresponding author
- gmcz7c@mail.mizzou.edu.
110 Southeastern Naturalist Vol. 10, No. 1
Stream salamanders often serve as the dominant vertebrate predators in
headwater streams (Davic and Welsh 2004) and may constitute a considerable
biomass reservoir in these systems (Hairston 1987, Peterman et al. 2008,
Petranka and Murray 2001). In the eastern United States, the majority of
stream-affiliated species are lungless salamanders of the family Plethodontidae,
and most have complex life cycles consisting of an aquatic larval stage
followed by a semi-terrestrial adult stage (Petranka 1998). In many cases,
larval salamanders may be more numerous than adults and likely represent a
larger part of the total species biomass (Davic and Welsh 2004). Because adult
stream salamanders rely entirely on cutaneous respiration, they are highly
susceptible to water loss across the moist, permeable surface of their skin and
may be forced to reduce their terrestrial activity until optimal temperature
and moisture conditions occur (Feder 1983). Being fully aquatic, the larvae of
these species may show fundamentally different responses to prevailing environmental
conditions.
Despite its ecological relevance in headwater stream systems, few studies
have examined the timing of daily activity patterns in larval salamanders (but
see Barr and Babbitt 2007, Orser and Shure 1975, Petranka 1984) and few studies
have specifically examined the environmental correlates of activity patterns
in stream salamanders (but see Johnson and Goldberg 1975). Although Orser and
Shure (1975) found surface densities of larval Desmognathus fuscus (Rafinesque)
Dusky Salamander to be no higher at night than during the day, a second study
(Petranka 1984) described a tendency for nocturnal activity in larval Eurycea
bislineata (Green) Northern Two-lined Salamander. Interestingly, another study
found that Northern Two-lined Salamander larvae demonstrated aperiodic
activity patterns until a fish predator was added, causing larvae to adopt primarily
nocturnal activity (Barr and Babbitt 2007). It is also possible that observed
nocturnal activity in certain salamanders is a product of light level (Placyk and
Graves 2001, Sites 1978), suggesting that periods of high cloud cover may also
correspond with increased activity.
Although numerous studies have demonstrated the sensitivity of terrestrial
amphibian activity to moisture and temperature conditions associated with
desiccation risk (e.g., Gibbons and Bennett 1974, Hairston 1949, Keen 1984,
Mazerolle 2001, Orser and Shure 1975, Semlitsch 1985, Todd and Winne 2006),
larval stream salamanders are entirely aquatic, and their foraging activity is not
physiologically limited by water loss. Johnson and Goldberg (1975) found a general
lack of larval salamander movement during high-flow periods, but observed
that larval activity peaked as flow levels stabilized following heavy rainfall.
Increased stream discharge following rainfall has been shown to increase invertebrate
drift (O’Hop and Wallace 1983), which represents a potential influx of
prey for larval salamanders. As a result, rainfall could function as an important
determinant of foraging activity. Finally, water temperature has been shown to
limit the swimming efficiency of salamanders (Marvin 2003a), suggesting that
low water temperatures may cause increased susceptibility to downstream drift
(Cecala et al. 2009) or an overall tendency for reduced activity.
2011 G.M. Connette, S.J. Price, and M.E. Dorcas 111
In this study, we used capture rates from a passive trapping technique as an
index of overall movement activity and sought to examine the influence of several
abiotic factors on the observed activity patterns of larval stream salamanders.
We consider the following factors to be potentially important as determinants of
larval salamander activity: time of day (day vs. night), temperature, rainfall, and
cloud cover. We used regression analysis and employed an information-theoretic
approach to model selection in order to determine which abiotic factors best predict
patterns of larval salamander activity.
Field-site Description
We captured salamanders in a first-order stream located in the Cowans Ford
Wildlife Refuge in Mecklenberg County, NC (35.3775°N, 80.9658°W). This
150-m perennial stream originates from two seeps and passes through a secondary,
mixed-hardwood forest before flowing into Mountain Island Lake (Cecala
et al. 2009). Five salamander species have been identified at this site during
previous research, including Northern Dusky Salamander, Southern Two-lined
Salamander, Eurycea guttolineata Holbrook (Three-lined Salamander), Pseudotriton
montanus Baird (Mud Salamander), and P. ruber Dunn (Red Salamander)
(K. Cecala, University of Georgia, Athens, GA, pers. comm.).
Methods
We sampled larval salamanders using one-liter plastic, inverted bottle funnel
traps (Willson and Dorcas 2003). Capture rates from passive sampling
techniques, such as funnel trapping, are dependent on both the density of
animals within the study area as well as the activity levels of those animals
(Willson and Gibbons 2009). Thus, capture rates from passive trapping would
reflect changes in activity levels during short-term studies where amphibian
population densities remain relatively constant (Willson and Gibbons 2009).
As a result, passive sampling using drift fences has been employed to study
both seasonal (Gibbons and Bennett 1974, Mazerolle 2001, Todd and Winne
2006) and daily (Semlitsch and Pechmann 1985) patterns of migratory activity
in pond-breeding amphibians, as well as environmental correlates of activity
patterns (Gibbons and Bennett 1974, Mazerolle 2001, Semlitsch 1985, Todd
and Winne 2006). Although the use of funnel traps has been previously used
to define patterns of surface activity in a pond-breeding salamander, Ambystoma
tigrinum (Green) (Eastern Tiger Salamander; Holomuzki and Collins
1983), this technique has not been applied to studies of the activity levels of
stream-associated salamanders. In this study, we used capture rates from funnel
traps (total number of captures per sampling period) as an index of overall
larval salamander activity.
Traps were arranged in one of four blocks containing ten traps each. Traps
within each block were arranged in pairs, with each pair containing one upstream-
facing and one downstream-facing trap. All pairs of traps within each
112 Southeastern Naturalist Vol. 10, No. 1
block were separated by 3 m. We conducted five trapping sessions between
4 October and 4 December 2007. During each trapping session, we checked
all traps within one hour of sunrise and sunset each day over the course of
four days. An individual captured before traps were checked at sunrise was
considered a night capture, whereas an individual captured before traps were
checked at sunset was considered a day capture. We alternated whether day
or night trapping was conducted first during each trapping session in order to
mitigate the influence of any behavioral changes resulting from previous sampling
intervals. For each individual captured, we recorded both the location
and orientation of the trap in which it was captured. Because we were interested
in examining the activity patterns of larval salamanders, the few adults
captured were excluded from all analyses. After measuring each salamander,
we released the individual 1 m downstream from the trap to ensure that it was
not washed immediately back into the trap. Total processing time did not exceed
two minutes per salamander.
To determine the effects of environmental variation on salamander activity,
we took measurements on a number of abiotic variables, including air temperature,
water temperature, recent rainfall, and cloud cover. Water temperature was
collected at 15-minute intervals by a datalogger (TidbiT v2, UTBI-001, Onset
Computer Inc., Pocasset, MA) which was submerged in the center of the stream.
Air temperature was also measured every 15 minutes with a datalogger (Hobo
Pro RH/Temp, H08-032-08, Onset Computer Inc.). Because of a high degree of
correlation between water temperature and air temperature (r > 0.92), we included
only mean water temperature in our analyses. The mean water temperature for a
trapping period was classified as the mean of all 15-minute intervals during the
approximately 12-hour period since traps were last checked. Recent rainfall was
recorded as the number of days since the last rainfall event. We also visually estimated
percent cloud cover from a nearby clearing each time we checked traps
and categorized these values as either low (<33%), medium (34–67%), or high
(>67%) (see Girard et al. 2003).
We modeled count data (total number of captures per visit) with a generalized
linear model following a Poisson distribution and created 15 a priori models
using combinations of four environmental variables to compare their ability
to predict stream salamander activity. We employed an information-theoretic
approach to model selection (Burnham and Anderson, 2002) to determine the
relationship between the abiotic variables and stream salamander activity. The information-
theoretic approach ranks each model based on the strength of evidence
for that model relative to the complete set of models. The models we constructed
were (1) GLOBAL (includes day vs. night, mean water temperature, cloud
cover, and days since last rainfall), (2) Night (day vs. night), (3) Temp (mean
water temperature), (4) Cloud (cloud cover), (5) Rain (days since last rainfall),
(6) RainCloudTemp, (7) RainCloudNight, (8) RainTempNight, (9) Cloud-
TempNight, (10) RainCloud, (11) RainTemp, (12) RainNight, (13) CloudTemp,
(14) CloudNight, and (15) TempNight.
2011 G.M. Connette, S.J. Price, and M.E. Dorcas 113
For each model, we calculated the QAICc value, which is a measure of the
strength of evidence for a given model, adjusted for overdispersion and small
sample size, as follows:
QAICc = -2(log - likelihood) / ĉ + 2K(K + 1) / (n - K - 1)
When calculating QAICc values for each model, we used the dispersion parameter
(ĉ) of the global model (Mazerolle 2006). We then calculated ΔQAICc for
each model, which is the difference in QAICc between each model and the best
model in the set. A ΔQAICc less than 2 suggests that there is substantial support
for the model, a ΔQAICc between 3 and 7 suggests that there is considerably less
support for the model, and a ΔQAICc greater than 10 suggests that the model is
very unlikely to best explain reality (Burnham and Anderson 2002). We also calculated
Akaike weights (ωi), which represent the probability that the given model
is the best among the entire set of candidate models.
We first developed models that incorporated data for all larval salamander
captures together and then developed species-specific models for larval
Red Salamander, which was the most commonly encountered species over
the course of our study. Because there were two or more models competing
for first place in both sets of analysis, we also calculated model-averaged parameter
estimates and corresponding unconditional standard errors for each
of the four environmental variables incorporated in the models (Burnham and
Anderson 2002, Mazerolle 2006). Confidence intervals were calculated for
each model-averaged parameter estimate as the individual estimate ± (1.96)
multiplied by the unconditional standard error for that estimate (Burnham and
Anderson 2002, Mazerolle 2006).
Results
We captured a total of 96 salamanders (17 adult, 79 larvae), representing
each of the five species known to be present at our study site: Dusky Salamander
(10 adult, 20 larvae), Southern Two-lined Salamander (3 larvae), Three-lined
Salamander (21 larvae), Mud Salamander (6 adult, 2 larvae), and Red Salamander
(1 adult, 33 larvae). Of the larvae captured in this study, 65 (82%) were
captured during night-trapping intervals, while 14 (18%) were captured during
day-trapping. We captured a mean of 1.92 ± 2.06 SD (n = 24) salamanders under
low cloud cover (0–33%), 1.00 ± 0.82 SD (n = 7) under medium cloud cover (34–
66%), and 4.78 ± 4.52 SD (n = 8) under high cloud cover. Traps facing upstream
captured only two more salamanders (n = 49) than those facing downstream (n =
47), suggesting that capture rates were not merely a product of downstream drift
(Bruce 1986). When larval captures from all species were combined, the model
incorporating cloud cover and time of day best predicted stream salamander
activity (ωi = 0.43; Table 1). There was also substantial support for the model
incorporating cloud cover, water temperature, and time of day (ωi = 0.19) and
the model incorporating cloud cover, rainfall, and time of day (ωi = 0.12). Of the
four variables considered, only the model-averaged parameter estimates for time
114 Southeastern Naturalist Vol. 10, No. 1
of day and cloud cover had 95% confidence intervals which did not overlap zero
(Table 3), indicating that these variables were the most important predictors of
larval salamander activity.
The models that best predicted larval Red Salamander activity consisted of
the model incorporating only time of day (ωi = 0.28), the model including cloud
cover and time of day (ωi = 0.19), the model including water temperature and
time of day (ωi = 0.18), and the model including rainfall and time of day (ωi =
0.14; Table 2). In the separate analyses for larval Red Salamanders, the top model
again had a low probability of being the “best” model among the entire set. Of
the model-averaged parameter estimates, only time of day had 95% confidence
Table 1. Regression models best explaining the influence of abiotic variables on captures of all
stream salamander larvae.
ModelA Log-likelihood KB QAICc
C ΔQAICc
D ωi
E
CloudNight -64.13 4 98.53 0.00 0.43
CloudTempNight -63.41 5 100.16 1.63 0.19
RainCloudNight -64.10 5 101.12 2.59 0.12
TempNight -66.42 4 101.72 3.19 0.09
Night -68.54 3 102.20 3.67 0.07
Global -63.04 6 102.42 3.89 0.06
RainNight -67.90 4 103.79 5.26 0.03
RainTempNight -66.42 5 104.34 5.81 0.02
Cloud -80.43 3 118.78 20.25 0.00
ANight: day vs. night, Temp: mean daily water temperature, Cloud: percent cloud cover at time of
sampling, Rain: days since last rainfall.
BParameters = number of variables + intercept + variance inflation factor (ĉ).
CQAICc values are based on the variance inflation factor of the global model.
DDifference between QAICc value of the current model vs. the best model.
EAkaike weight. Probability that the model is the best among the set of all candidate models.
Table 2. Regression models best explaining the influence of abiotic variables on captures of larval
Pseudotriton ruber (Red Salamander).
ModelA Log-likelihood KB QAICc
C ΔQAICc
D ωi
E
Night -46.34 3 74.63 0.00 0.28
CloudNight -45.15 4 75.37 0.74 0.19
TempNight -45.23 4 75.49 0.86 0.18
RainNight -45.63 4 76.08 1.45 0.14
CloudTempNight -44.60 5 77.18 2.55 0.08
RainCloudNight -45.00 5 77.77 3.13 0.06
RainTempNight -45.13 5 77.97 3.34 0.05
Global -44.59 6 79.96 5.32 0.02
Cloud -53.45 3 85.07 10.43 0.00
ANight: day vs. night, Temp: mean daily water temperature, Cloud: percent cloud cover at time of
sampling, Rain: days since last rainfall.
BParameters = number of variables + intercept + variance inflation factor (ĉ).
CQAICc values are based on the variance inflation factor of the global model.
DDifference between QAICc value of the current model vs. the best model.
EAkaike weight. Probability that the model is the best among the set of all candidate models.
2011 G.M. Connette, S.J. Price, and M.E. Dorcas 115
intervals which did not overlap zero (Table 3). The magnitude and precision of
this parameter estimate suggests that time of day was a strong predictor of Red
Salamander activity, indicating a clear tendency for nocturnal activity in larvae
of this species.
Discussion
The goal of this study was to determine important abiotic correlates of activity
in larval stream salamanders. We found that time of day (e.g., day vs. night)
appeared to be the best predictor of stream salamander activity in the models
we compared. In the separate analyses for both Red Salamander and all species
combined, we observed a clear tendency for nocturnal activity. This pattern of
nocturnal activity has been consistently demonstrated for adult stream salamanders
(Barbour et al. 1969, Hairston 1949, Orser and Shure 1975, Shealy 1975),
as well as in a previous study of larval stream salamanders (Petranka 1984). Although
Orser and Shure (1975) found larval salamander densities to be no higher
at night than during the day, Barr and Babbitt (2007) observed a shift towards increased
nocturnal activity in the presence of a fish predator. This suggests that the
extent of nocturnal behavior for some salamanders may be partially a response
to predator cues. Daily peaks in adult stream salamander activity have also been
found to coincide with the peak activity of their potential prey (Holomuzki 1980).
Larval salamanders consume primarily aquatic invertebrates (Cecala et al. 2007,
Davic 1991, Petranka 1984), which may show daily periodicity in drift, with
peaks in abundance occurring at either dusk (Elliot 1967) or dawn (Waters 1972).
Thus, foraging during these periods may allow stream salamanders to optimize
their resource acquisition while limiting their vulnerability to predators in comparison
to daytime activity.
Previous studies have also proposed that nocturnal behavior in adult salamanders
may be a direct response to light level (Placyk and Graves 2001, Shealy 1975),
suggesting that low light conditions may also correlate with an increase in activity.
One possible explanation for increased activity under low-light conditions is
Table 3. Model-averaged parameter estimates and 95% confidence intervals for all variables included
in analysis.
ParameterA Model-averaged estimates Lower 95% C.I. Upper 95% C.I.
NightTotal 1.50 0.81 2.20
TempTotal 0.05 -0.03 0.13
CloudTotal 0.35 0.05 0.65
RainTotal 0.01 -0.08 0.09
NightPserub 1.71 0.60 2.83
TempPserub 0.06 -0.05 0.18
CloudPserub 0.26 -0.18 0.70
RainPserub -0.04 -0.16 0.08
ATotal: parameter estimates from count data including all individuals captured, Pserub: parameter
estimates from count data of all P. ruber captures.
116 Southeastern Naturalist Vol. 10, No. 1
that salamanders forgo active foraging during high light levels in order to reduce
their risk of predation (Madison et al. 1999). Alternatively, salamanders may
conduct passive, sit-and-wait foraging until low light levels preclude the use of
visual cues (Placyk and Graves 2001). This trend only achieved significance in
our analysis of combined larval captures, and the magnitude of the parameter
estimate for cloud cover was greatly surpassed by the estimate for time of day
in both sets of analyses. Furthermore, the higher observed capture rates under
high cloud cover conditions were primarily driven by much higher capture rates
during a handful of night-trapping intervals. Because only five trapping intervals
took place during conditions of high nighttime cloud cover, we feel that further
research is required to determine whether differing light levels between nights
correlate significantly with variability in larval salamander activity.
Warm temperatures are needed to sustain both metabolic rate and growth rate
in amphibians (Beachy 1995, Fitzpatrick 1973), and low temperatures have also
been shown to cause a significant reduction in locomotor performance, especially
endurance (Else and Bennett 1987; Marvin 2003a, b). Ashton (1975) reported
that Northern Dusky Salamanders began moving into sub-surface winter retreats
when stream temperature dropped below 7 °C, and Orser and Shure (1975) noted
a decrease in activity when water temperature dropped below 12 °C. Mean water
temperatures during our study ranged from 8.1 °C to 19.5 °C. Although no adult
salamanders were captured when water temperatures dropped below 14 °C, our
analyses found no response of larval salamanders to water temperature. In fact,
29% of larval captures (n = 23) occurred during trapping intervals where mean
water temperature fell below 14 °C. Even the highest water temperatures observed
during our study (19.6 °C) fell within the thermal preferences of the adults
of many stream salamander species (Spotila 1972). Cecala et al. (2007) found a
negative relationship between water temperature and the presence of prey items
in larval salamander stomach contents. They proposed that this could be due to
reduced foraging activity at warm water temperatures or limited food availability
during the summer months. Our study was conducted during the fall and found
that overall, larval salamander activity was not related to water temperature. Our
study, however, did not distinguish between local foraging activity and other
in-stream larval movements such as long-range movements, which can exceed
100 m for Red Salamander (Cecala et al. 2009).
Unlike many previous studies of amphibian movement and activity patterns
(Barbour et al. 1969, Gibbons and Bennett 1974, Keen 1984, Orser and Shure
1975), the results of our study were based entirely on captures of larval salamanders,
which exhibit movement patterns free from physiological limitation by
moisture conditions. Although rainfall could be a potential cause of downstream
drift in larval salamanders, one previous study found that larvae exhibited a
peak in movement as stream flow stabilized following periods of high flow rate
(Johnson and Goldberg 1975). Because salamanders were less active during peak
stream discharge (Johnson and Goldberg 1975), it is likely that downstream drift
is not accidental, but could instead be a density-dependent dispersal mechanism,
2011 G.M. Connette, S.J. Price, and M.E. Dorcas 117
as proposed by Bruce (1986). This resilience to accidental drift was also suggested
by the upstream-biased dispersal described by Lowe et al. (2003). Cecala
et al. (2009) also found upstream-biased dispersal in large Red Salamander larvae
and found that no size class demonstrated downstream-biased movement. In
this study, upstream- and downstream-facing traps captured nearly equivalent
numbers of salamanders. Although we hypothesized that an influx of prey items
following rainfall could also trigger an increase in larval salamander foraging
activity, we found no effect of rainfall on the observed capture rates of larval
salamanders.
Understanding activity patterns is a critical step towards understanding the
way in which organisms relate to their environment and interact with other members
of their community. Activity patterns of larval salamanders are particularly
of interest due to the fact that larvae are often more numerous than adults and
likely represent a larger part of the total salamander biomass in many systems
(Davic and Welsh 2004). Furthermore, variation in activity may also have an
important influence on individual detection probabilities and the effectiveness of
population-monitoring techniques (Orser and Shure 1975, Peterson and Dorcas
1992). This study demonstrated a clear tendency for nocturnal activity in larval
stream salamanders and also found a weak positive correlation between activity
levels and cloud cover. Furthermore, we found that larval stream salamanders
may remain active across a broader range of environmental temperatures than
adults in our study system. We hope that our work leads to future research on
stage-specific variation in salamander activity patterns.
Acknowledgments
We thank B. Graham for assisting with animal collection and S. Pittman, M. Mackey,
K. LaJeunesse, P. Peroni, and K. Cecala for advice and comments on this manuscript. We
also thank the Mecklenburg County Natural Resources Division for assistance with this
study. This research was approved by the Davidson College Institutional Animal Care
and Use Committee (Protocol 3-04-11). Manuscript preparation was aided by the Environmental
Remediation Sciences Division of the Office of Biological and Environmental
Research, US Department of Energy through Financial Assistance Award DE-FC09-
96SR18546 to the University of Georgia Research Foundation. Funding was provided
by the Department of Biology at Davidson College, Duke Power, and National Science
Foundation grants (DBI-1039153 and DEB-0347326) to M.E. Dorcas.
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