Behavioral Ecology Vol. 12 No. 1: 51–58
A comparative study of activity levels in larval
anurans and response to the presence of
different predators
Jean M. L. Richardson
Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA
Activity level is a key behavioral trait in many animals which mediates a trade-off between finding food and avoiding predation.
Optimal activity level will therefore depend on environment, and plasticity in response may increase fitness (if an organism
encounters multiple environments in a lifetime). One group in which activity level, and its relationship to foraging and predation
risk, has been well studied is larval anurans. Anurans inhabit a range of distinct freshwater aquatic community types that are
created by differences in pond permanency and top predator. Species segregate across these pond types and therefore tadpoles
from different species encounter different selection regimes. I hypothesized that species from different pond types would
therefore differ in activity behavior, and in plasticity of this behavior. I tested this in a phylogenetic framework to consider the
evolution of plasticity in anurans diversifying into different pond types. Time spent active was quantified for larvae of each of
13 anuran species (from three taxonomic families) in four conditions: when no predator was present, and in the non-lethal
presence of a dragonfly, newt, or fish predator. Species nested within pond type by taxonomic family differed significantly in
time spent active. A significant interaction between predator treatment and taxonomic family was also observed. A phylogenetic
analysis of change in behavior revealed strong positive correlations in evolution of these behaviors and suggests constraints on
the ability of larval anurans to independently modify activity levels in the presence versus absence of predators. Key words:
Anura, behavior, activity, anti-predator behavior, comparative study, tadpoles, phenotypic plasticity. [Behav Ecol 12:51–58 (2001)]
A
ll organisms face a trade-off between finding food and
becoming food for a predator (Abrams, 1991; Lima,
1998; Lima and Dill, 1990; Werner and Anholt, 1993). In
many animals, this trade-off is mediated through behavioral
changes in activity levels (Anholt and Werner, 1995; Skelly,
1994). The trade-off is intuitive: individuals increase the
chances of finding food as they move about more actively, but
in doing so they also increase the chances of being detected
by a predator. Theoretical models that incorporate these
trade-offs predict optimal activity levels that vary with food
availability and predator presence (Abrams, 1991; Werner and
Anholt, 1993). These models therefore predict that species
encountering different selective regimes will show species-specific differences in activity level. For example, those species
found in habitats where the selective force of predation risk
is strong relative to the selective force imposed by food limitation, are predicted to be relatively less active than those species found in environments where food limitation is relatively
more important as a selective agent.
Further, since both food levels and predation risk can vary
temporally within an organism’s lifespan, and because behavior is labile, we might expect that an optimal organism would
display behavioral plasticity for the trait. Indeed, empirical
data show this can often be observed (reviewed in Lima,
1998). For example, Lawler (1989) showed that amount of
movement was significantly lower in tadpoles when a predator
was present. We can quantify plasticity in traits by viewing
traits expressed in each environment as separate traits and
measuring the across environment correlation of the traits;
this is the approach taken in quantitative genetic models of
Address correspondence to J. M. L. Richardson, Department of Zoology, University of Toronto, 25 Harbord St., Toronto, Ontario M5S
3G5, Canada. E-mail: [email protected].
Received 18 November 1999; revised 18 May 2000; accepted 26 May
2000.
2001 International Society for Behavioral Ecology
the evolution of plasticity (Falconer, 1952; Scheiner, 1993; Van
Tienderen and Koelewijn, 1994; Via and Lande, 1985). Both
quantitative genetic and optimality models predict that, for
labile traits, plasticity will always be favored (reviewed in Scheiner, 1993). However, predictions of optimal plasticity assume
no cost to plasticity and sufficient genetic variance (Scheiner,
1993). Since these assumptions are unlikely to be true, we
might expect that organisms evolving under different selective
regimes may display differing levels of phenotypic plasticity.
The influence of selective regime and shared ancestry on
the evolution of traits (including plasticity) can be assessed
using a comparative study (Huey, 1987; Pagel and Harvey,
1988). Comparative studies require a system in which data can
be collected from several species, of known phylogenetic relationship, experiencing different selection regimes. Larval
anurans provide an ideal study system for consideration of the
evolution of plasticity in related species evolving under different selective regimes. Breeding anurans tend to have nonoverlapping distributions along a gradient of pond permanency. At one end of this gradient are ephemeral ponds that
dry annually, while at the other end of the spectrum are permanent ponds and lakes. Correlated with these changes in
hydroperiod are changes in predators (McPeek, 1990a, b;
Skelly, 1997; Wellborn et al., 1996). Variation in hydroperiod
and predators creates different selective forces for organisms
in ponds from different positions along the gradient. Activity
level affects both foraging and predator avoidance traits, and
is a behavioral trait known to show plasticity in response to
environmental conditions. Therefore, activity level is expected
to reflect the differences in habitat type across the gradient
of pond permanency (Collins and Wilbur, 1979; Skelly, 1997;
Wellborn et al., 1996). For example, in permanent ponds with
fish, fish predation is a dominant selective force (e.g., McPeek, 1990a, b), and the permanency of the pond allows a
strategy of slow growth rates to be successful. Thus, tadpoles
using these fish ponds should have relatively lower activity
rates than species using ponds with less predation pressure.
Behavioral Ecology Vol. 12 No. 1
52
Figure 1
Phylogenetic relationships among species used in this study. Lengths
of tree branches do not reflect true branch lengths. The tree is
based on electrophoretic (Hylidae) and DNA (Ranidae, Bufonidae)
data from Hedges (1986); Hillis and Davis (1986); and Graybeal
(1997), respectively. Bold letters indicate habitat type where larvae
are found: V, vernal; D, dragonfly; F, fish; M, multiple (see text for
explanation of pond types).
Species using ponds with more variable predation pressure,
such as ponds with aquatic insect larvae as top predators
whose threat varies as the cohort matures, should be more
likely to display plasticity in activity levels than those species
using ponds where predation risk is relatively constant.
Larval anurans are also an ideal study system for addressing
questions about the evolution of plasticity because we have
good information on phylogenetic relationships among species in the three most speciose families in North America (Hylidae, Ranidae, Bufonidae; Figure 1). Species in each of these
three families have diversified into several different pond
types (see Methods for characterization of pond types). Thus,
the group provides replicate divergence into similar habitat
types, allowing tests of phenotypic change corresponding to
habitat type. Furthermore, previous studies showing differences in activity levels among anuran species, as well as differences among species in behavioral response to predators
(Anholt et al., 2000; Lawler, 1989; Skelly, 1995; Woodward,
1983) suggest activity level is a trait which has been influenced
by selection. Decreased activity in tadpoles reduces predation
risk (Anholt and Werner, 1995; Skelly, 1994).
In the present study, I quantify time spent active in larvae
of 13 anuran species when no predator is present and in the
presence of different predators. I test for effects of lineage
and habitat type on tadpole behavioral responses. Because activity rates influence both predation risk and foraging rates
(Anholt and Werner, 1995), I expect tadpoles from vernal
ponds to be most active and those from fish ponds to be least
active. I quantified time active in the laboratory using four
treatment levels: (1) no predator present; (2) presence of a
dragonfly larva (Anax junius); (3) presence of a newt (Notophthalmus viridescens); and (4) and presence of a sunfish
(Lepomis spp.). The three species represent the most common
predators encountered by tadpoles in the field across the
pond types: Anax and Notophthalmus in dragonfly ponds, and
Lepomis in fish ponds (Werner and McPeek, 1994). Quantifying activity of each anuran species in the presence of each
predator allowed me to test whether species differentially responded to predators found in the same pond type versus
those found in different pond types, and also provided data
on whether species differed in plasticity for this trait.
Figure 2
Species used in this study grouped by taxonomic family and the
habitat in which tadpoles are found. State in which experiments
were performed for each species is given in parentheses (NH ⫽
New Hampshire, FL ⫽ Florida).
METHODS
From 1996 to 1998, I collected tadpoles of 13 anuran species
from eastern North America. Late stage tadpoles (Gosner
stages 34–38, Gosner, 1960) were caught by dipnet, returned
to the lab, and housed in 38 liter plastic tubs (36 ⫻ 50 ⫻ 21
cm) until placed in experimental tanks. Tadpoles were kept
in 50% filtered pond water and 50% aged tap or well water;
the same water was used in all experiments. I fed animals
‘tadpole food’ (pellets of processed grain and plant products
supplemented with vitamins; Carolina Biological Supply Company, Burlington, NC, USA). Water was changed as needed
(e.g., whenever water became fouled), typically every 4–7 days.
All species thrived under this regime. All tadpoles used in this
study were at Gosner stage 36–38 (i.e., in hindlimb development; Gosner, 1960). I selected this stage because previous
work has shown that species differences in activity level are
greatest at this developmental stage (Lawler, 1989).
To maximize the number of species from which I could
collect data, I conducted experiments in New Hampshire,
USA, during 1996 and 1998 (six species), and in Florida, USA
during 1997 (seven species) (species listed in Figure 2). Tadpoles from New Hampshire, USA were housed at Dartmouth
College in a room maintained at 20⬚C on a 14:10 light:dark
cycle. Species from Florida were housed in the Florida State
University Mission Road Greenhouse facility. The greenhouse
received natural daylight and was maintained within a temperature range of approximately 23–27⬚C with heating elements and cooling fans; on rare occasion the temperature
rose to between 28–30⬚C, but water temperature in greenhouse tanks was never greater than that measured in local,
permanent ponds on the same day.
Tadpoles inhabit ponds that vary along a gradient of pond
permanency; this gradient has been well described and, although it is continuous in nature, two clear transitions are
seen along the gradient (Semlitsch et al., 1996; Skelly, 1997;
Wellborn et al., 1996). Wellborn et al. (1996) termed these
the permanence transition and the predator transition and
these two transitions lead to three categories of pond which
they label as ‘‘Temporary habitats,’’ ‘‘Permanent, fishless habitats,’’ and ‘‘Habitats with fish’’ (see Wellborn et al., 1996:
Figure 2). The difference in prey species’ assemblage between
permanent fishless and fish ponds has been well established
for many different taxonomic groups (e.g., cladocerans;
Brooks and Dodson, 1965; odonates; McPeek, 1990a, b; anurans; Collins and Wilbur, 1974; Morin, 1983; Skelly, 1995;
Werner and McPeek, 1994). Evidence suggests that it is the
Richardson • Activity in tadpoles
change in top predators which leads to differential species’
assemblages among these two types of permanent ponds (reviewed in Wellborn et al., 1996) and thus I will refer to habitats with fish as ‘‘fish ponds’’ and permanent habitats without
fish as ‘‘dragonfly ponds’’ to emphasize the role of large
aquatic insects, such as dragonfly larvae, as the top predators
in permanent fishless ponds (McPeek, 1990a, b; Skelly, 1996;
Werner and McPeek, 1994). Note that pond permanency is a
gradient and so that, although relatively permanent, dragonfly
ponds may dry on occasion (i.e., during drought conditions).
At the extreme of the gradient, on the other side of the ‘‘permanence transition,’’ are extremely temporary habitats; these
dry predictably every year and I will subsequently call these
‘‘vernal ponds.’’
I used these broad categories to classify the tadpole species
I collected according to pond type. Although I did not quantify predators in the ponds from which I collected, I sampled
the ponds from which I collected tadpoles extensively for the
9 months I was in Florida and for two breeding seasons in
New Hampshire. I had strict and repeatable criteria for the
classification of pond types. Vernal ponds were only those habitats that were dry both in the winter preceding the breeding
season and in the winter after the breeding season; these were
shallow bodies of water (less than two feet deep) that would
clearly not overwinter. In other words, a pond was only considered vernal if it dried predictably every year. In Florida,
these were mostly low lying areas that filled with water during
the rainy season. In New Hampshire, these included rock
pools that filled in spring when rivers overflowed the bank
and deep tire tracks that filled with rain or snow melt. I classify
H. squirella as a vernal pond species because I found it only
in such ponds; in my sampling of over 30 ponds in Florida, I
never collected H. squirella in dragonfly ponds. In New
Hampshire, the only species I found in such vernal ponds was
B. americanus (which is also found in fish ponds). Dragonfly
ponds were those ponds that held water both before and after
the breeding season, had had water continuously for several
years prior, and had no predatory fish (personal observation;
M. McPeek, personal communication for New Hampshire
ponds; J. Travis, personal communication for Florida ponds).
Fish ponds are the most permanent ponds and all contained
predatory fish (personal observation; M. McPeek, personal
communication; J. Travis, personal communication). After 2
years of drought, subsequent to this study, the fish ponds in
northern Florida from which I collected all still had water,
while the dragonfly ponds from which I collected all became
dry. Species that I classified as dragonfly pond species were
found in many different dragonfly ponds, but were never
found in fish ponds. Likewise, fish pond species were found
in several different fish ponds, but never in dragonfly ponds.
Some species were consistently found in two of these pond
types. For example, B. americanus was found both in vernal
ponds and in fish ponds throughout my sampling area in New
Hampshire. Pseudacris crucifer were found in both dragonfly
and fish ponds that I sampled in both New Hampshire and
Florida. Species such as these that were found in two different
pond types were labeled ‘‘multiple pond’’ species. Thus, I
have four levels of habitat type: vernal, dragonfly, fish, and
multiple pond species. For this study, individuals from each
species were collected from several different sites. In 4 years
of sampling ⬎ 30 sites for the species found in this study, I
never encountered species in a pond type inconsistent with
my categorization of them, using the criterion for pond types
outlined above. Further, my classification of species is consistent with classifications used in previously published work
(e.g., Skelly, 1996 [P. crucifer]; Werner and McPeek, 1994 [R.
clamitans, R. catesbeiana]).
Experiments were run in the same room in which tadpoles
53
were housed, using 40-l aquaria. Each aquarium was divided
into two compartments using a fiberglass screen stiffened at
each side with doweling and fastened with silicon to the insides of each aquarium approximately 10 cm from one end.
Predators, when present, were placed in the smaller section
and tadpoles were placed in the larger section of the tank;
the screen divider allowed flow of water and water-borne
chemical cues from the predator to the tadpole side, but prevented predators from preying on tadpoles. Aquaria had a 3
cm layer of washed sand as substrate and Elodea-style artificial
plants as places of refuge for the tadpoles. Tanks (along with
sand and artificial plants) were randomly assigned to predator
treatments at the start of experiments and then dedicated to
that predator treatment (to guard against the transfer of predator chemical cues). For filming, tanks were set up on four
metal shelving units with each shelf holding two aquaria.
Eight tanks were set up simultaneously to allow filming of two
complete replicates at a time.
For each trial, aquaria were randomly assigned to shelves.
Aquaria were filled with approximately 22 l of water. I divided
the tadpole side of the aquaria into four quadrates and placed
two stems of artificial plants haphazardly in two diagonally
opposed quadrates. Plants and sand were well rinsed with water and shelves and tank positions were re-assigned to treatments every second trial. I placed tadpoles and predators in
the aquaria 18 to 24 h prior to recording tadpole behavior to
allow tadpoles to acclimate to aquaria. I also added a small
piece of pressed algae disc (Wardley Spirulina discs) as food
for tadpoles. Predators were given no food for the duration
of the trial.
Three tadpoles were placed in each aquarium; this mimics
natural densities for most tadpoles (Morin, 1983). However,
for some species I was unable to collect enough tadpoles at
the correct stage to do this; thus, some species were tested
using only two individuals per tank (A. gryllus, R. catesbeiana,
and some replicates of H. gratiosa) or only one individual per
tank (R. sphenocephala). I checked for an effect of number of
individuals per tank on activity behavior using H. gratiosa (because trials were done in this species with both two and three
individuals per tank) and found no effect (F1,8 ⫽ 0.18, p ⬎
.68). Nonetheless, tadpoles respond to the presence of conspecifics and thus R. sphenocephala were tested under somewhat different conditions than all other species; this should
be borne in mind when interpreting results.
A blind shielded all tanks from disturbance during the acclimatization period. Behavior was filmed using a video camera and video cassette recorder. Two tanks were filmed simultaneously. Each tank was recorded for a 15 min period
and then the camera was moved to the next position (shelves
were assigned to treatment at random, and then filmed in
order). In 1996 at Dartmouth, the light cycle of tadpoles was
switched so that for each species half the trials were done in
the morning and half were done in the evening. Analysis of
these results revealed no effect of time of day and so subsequently (1997 and 1998) the light cycle was kept constant. All
trials were started approximately 2 h after the lights switched
on and took approximately 75 min to complete.
Fish predators were collected from local lakes by seining,
housed in 40-l aquaria in the lab, and fed a combination of
invertebrate prey, earthworms and TetraBits (Tetra, Blacksburg, VA, USA). The pumpkinseed sunfish, Lepomis gibbosus,
was used in New Hampshire experiments and the spotted sunfish, Lepomis punctatus in the Florida experiments. Notophthalmus (Notophthalmus viridescens viridescens in New Hampshire and N. v. louisianensis in Florida) were also collected
from local populations by dipnet, housed in 20-l aquaria and
fed invertebrate prey collected from local ponds. Anax junius
larvae in the final or penultimate stage were collected by dip-
Behavioral Ecology Vol. 12 No. 1
54
net, kept individually in small plastic tubs (9 ⫻ 9 ⫻ 9 cm)
and fed invertebrate prey or small fish. Anax were collected
throughout the season to replace individuals as they matured.
No individual predator was used more than twice with the
same anuran species; in the majority of cases predators were
used only once with each species.
All experimental protocols were approved by the Animal
Care and Use Committees of Dartmouth College (protocol
98–03–10) and Florida State University (protocol 9701).
Statistical analyses
Time spent active was quantified using a simple event recording program I wrote to keep track of the length of time active
by one, two, or three tadpoles. A tadpole was considered active
whenever it was moving through the water column in any direction. Tadpoles grazing, but not actually moving any distance were not considered active, because identifying whether
or not a tadpole remaining in one spot was actually grazing
was difficult. Previous research in both Bufo and Hyla species
has found that swimming is positively correlated with growth
rate (Skelly, 1996; Skelly and Werner, 1990).
Mean time active (s) per tank (out of a possible 900 s) was
used as the response variable in analyses after transformation
with the natural logarithm to make the data normally distributed. I looked for differences in mean time active using a
three-way ANOVA with taxonomic family, habitat, and predator treatments as fixed effects and species nested within family
by habitat as a random effect. Thus, the appropriate error
term for habitat and family effects was species(family by habitat).
Evolutionary contrasts
Species’ trait values are influenced by shared common ancestry and thus species can not be considered independent data
points (Felsenstein, 1985). Many comparative methods have
now been developed to statistically control for this non-independence (reviewed in Felsenstein, 1988; Gittleman and Luh,
1992; Harvey and Pagel, 1991; Huey, 1987). Felsenstein’s independent contrast method has been repeatedly found the
most unbiased and robust of these methods (Dı́az-Uriarte and
Garland, 1996, 1998; Gittleman and Luh, 1992; Martins and
Garland, 1991). Felsenstein’s independent contrasts transform species’ trait means into n-1 independent contrast values
(Felsenstein, 1985). Further, these contrast values, when standardized by branch length, actually estimate evolutionary
rates of the traits in question (Martins, 1994; McPeek, 1995).
I therefore used these contrast values to examine correlations
in the evolutionary change of activity level in the presence of
different predators (i.e., I considered time active under each
treatment level a trait, giving four trait values per species).
Felsenstein’s independent contrast method depends on
knowledge of the branch lengths in the phylogeny and requires that all branch lengths be measured on the same scale
(Martins, 1996). The phylogenetic tree I use for anurans (Figure 1) was put together from three different studies (one for
each family clade), each of which used a different molecular
technique (Graybeal, 1997; Hedges, 1986; Hillis and Davis,
1986). Thus, the branch lengths for each clade are clearly not
based on the same scale (i.e., we can not assume that proteins
and DNA evolve at the same rate). Therefore, I could not use
branch lengths from these published phylogenies in a combined comparative analysis. Although it is possible to assign
branch lengths arbitrarily (Garland et al., 1992; Grafen, 1989),
this makes assumptions about character evolution that may or
may not be true. Instead I chose to test correlations in evolutionary change using a randomization procedure that would
be robust to branch length differences, allowing me to determine which correlations are present independent of relative
branch lengths (Losos, 1994; Martins, 1996). In this way, my
tests make no assumptions about relative branch lengths. I did
this by calculating contrasts on trees with branch lengths assigned random values between zero and one. I calculated contrast values using this tree with randomly assigned branch
lengths for two traits and then calculated the correlation coefficient between the contrasts of the two traits. This procedure was repeated 2000 times for each pair of trait values and
a distribution of contrast correlations was generated. A confidence interval for each correlation was determined by ranking the resultant correlation coefficients and finding the two
values that encompassed 95% of the values (Manly, 1991).
RESULTS
Family and habitat effects
I collected data for this study from two potentially different
conditions: in Florida and in New Hampshire. Comparative
studies depend on including as many species as possible (Pagel and Harvey, 1988) and thus the benefits of collecting data
on more species in Florida outweighed the inherent problems
of combining data from two different locales. To ensure that
data were not systematically biased by the locale in which they
were collected, I tested for a locale effect using species nested
within locale as the error term (since species differed significantly). This ANOVA found no significant effect of locale on
time active (F1,11 ⫽ 4.12, p ⬎ .067). The power of this test to
detect an effect of the size seen (mean time active differed by
138 s between groups) is relatively low (⬃ 0.50), but a thorough inspection of the data revealed no systematic effects of
locale that might affect results described here. No interaction
was present (using species nested within the interaction term
as the denominator) between locale and predator (F3,44 ⫽
0.30, p ⬎ .82), locale and family (F2,7 ⫽ 0.36, p ⬎ .71), or
locale and habitat (F2,6 ⫽ 0.05, p ⬎ .95). Also, the main effects
of family, habitat and predator when tested on each locale
separately were very similar to presented results of combined
data (interaction between family and habitat could not be tested in these analyses because of too many missing cells in the
data). Therefore, in all results below I combine species from
both locales.
Species nested within habitat by family treatments differed
strongly in time spent active (Table 1, Figure 3). Time spent
active also varied significantly by family, and by predator, and
a significant interaction between family and predator was present (Table 1). The effect of habitat was not statistically significant (Table 1). Because the full design has several missing
cells (not all families have species in all habitats; Figure 2), I
also tested a fully crossed subset of the data to confirm results
from the unbalanced dataset. Results from an ANOVA of this
subset of data (ranids and hylids in dragonfly, fish, and multiple ponds) were very similar to those from the whole dataset.
The presence of strong species differences within a family
by habitat combination makes interpretation of the dataset
more challenging. Although the habitat effect was not significant in the above analysis, this tells us only that on average
species’ responses tend to be similar across habitats. This
could mean that every species in a family has a similar trait
value, no matter which habitat they use. However, we would
also get this result if species in one habitat type had low and
high trait values, while species in another habitat type had
medium trait values. The strong species within habitat by family effect made the latter result seem highly likely for these
data. Therefore, to interpret the data better, I ran analyses on
differences between species.
Richardson • Activity in tadpoles
55
Figure 3
Activity levels when no predator present (mean ⫾ SE). Species are
labeled as follows: Hsq, H. squirella; Hfem, H. femoralis; Hv, H.
versicolor; Hg, H. gratiosa; Rcl, R. clamitans; Rs, R. sylvatica; Hcin,
H. cinerea; Rcat, R. catesbeiana; Ag, A. gryllus; Pc, P. crucifer; Ba, B.
americanus; Bt, B. terrestris; Rsph, R. sphenocephala.
First, I looked for species’ differences in time spent active
when no predator is present. I used activity when no predator
is present as ‘‘baseline’’ activity because species are expected
to maximize activity levels under this condition, and it provides a controlled environment that allows species’ comparisons. A one-factor ANOVA confirmed strong species differences in time spent active (F12,68 ⫽ 11.93, p ⬍ .0001). Tukey
tests found significant differences mostly between the least active species, A. gryllus, and the most active species, R. sylvatica, and other species (Table 2). Looking at both the Tukey
test results (Table 2) and Figure 3 suggests that A. gryllus, P.
crucifer, (multiple pond species) and H. squirella (vernal pond
species) can be grouped as the least active, while R. clamitans
and R. sylvatica (dragonfly pond species) can be grouped as
the most active species.
To consider how species differ in behavioral response to
predators, I analyzed predator effects within each species
(each species is a separate experiment in my study). These
analyses found significant changes in time active with changing predators for the vernal pond species H. squirella (F3,13 ⫽
4.06, p ⬍ .040) and the dragonfly pond species R. sylvatica
(F3,28 ⫽ 4.35, p ⬍ .012), and a nearly significant effect for the
dragonfly pond species H. versicolor (F3,8 ⫽ 3.67, p ⬍ .063).
No other species revealed significant predator effects (Figure
4), but across species the presence of predators significantly
Table 1
ANOVA table for full model analyses
Source
DF
MS
F
p
Spp(family·habitat)
Family
Habitat
Family·Habitat
Predator
Family·Predator
Habitat·Predator
Family·Habitat·Predator
5
2
3
2
3
6
9
6
14.69
95.53
31.12
0.33
4.44
2.78
1.93
1.71
12.15
6.50*
2.12*
0.02*
3.67
2.30
1.60
1.41
0.0001
0.0406
0.2166
0.9778
0.0132
0.0366
0.1187
0.2113
Ln(time active) is the response variable.
* These F-ratios use spp(habitat·family) MS as the denominator.
Figure 4
Proportional change ([time active with predator present—time
active with no predator present]/time active with no predator
present) in time spent active in the non-lethal presence of Anax,
newt, and fish predators. Proportional change is calculated based
on the mean of each treatment and therefore no error bars are
present. The horizontal line at y ⫽ 0 divides the graphical space
into a decrease in activity with predators (below the line) and an
increase in activity with predators present (above the line).
reduced time active (Table 1; Tukey’s test revealed the no
predator and Notophthalmus treatments differed from the Lepomis treatment). Note that failure to detect more within species differences in activity in the presence of predators may
be due to low power. Using the error MS for each species to
determine the power of this design to detect a predator effect
of 100 s (⫽ 11% of the time observed), I found that power
ranged from approximately 10% for R. catesbeiana to nearly
100% for H. versicolor (Zar, 1984). Power to reject the null
hypothesis under this alternative hypothesis was also high (⬎
Behavioral Ecology Vol. 12 No. 1
56
Table 2
Species differences in time active when no predator is present
Rcl Rsph Bt
Hg Ba Hv Rcat Hcin Hfem Pc Hsq Ag
Rs
Rcl
Rsph
Bt
Hg
Ba
Hv
Rcat
Hcin
Hfem
Pc
Hsq
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Asterisks indicate those species which differ significantly at p ⫽ 0.05
using Tukey’s test. Species names are abbreviated as follows: Rs, R.
sylvatica; Rc,l R. clamitans; Rsph, R. sphenocephala; Bt, B. terrestris;
Hg, H. gratiosa; Ba, B. americanus; Hv, H. versicolor; Rcat, R.
catesbeiana; Hcin, H. cinerea; Hfem, H. femoralis; Pc, P. crucifer; Hsq,
H. squirella; Ag, A. gryllus.
98%) for B. terrestris, R. clamitans, and P. crucifer. Power
ranged from 0.25 to 0.45 for B. americanus, H. cinerea, H.
femoralis, H. gratiosa, and R. sphenocephala.
Evolutionary contrasts
Contrast values for time spent active in the different predator
treatments revealed strongly significant positive correlations
(Table 3). This was true for all possible pairings of the four
sets of contrast values. Time active with Lepomis and time active in each of the other treatments showed the strongest relationships. Time active with Notophthalmus and time active
when no predator was present were relatively weakly correlated, with a much wider confidence interval than the other contrast correlations (Table 3).
DISCUSSION
This study confirms the expected presence of strong species
differences in activity levels, and that tadpoles modify activity
levels in the presence of a predator. However, phylogenetic
analyses revealed tight positive correlations in the evolution
of activity levels under different predator conditions. Thus,
species that evolve to be more active when alone also evolve
to be more active in the presence of predators. This crossenvironment correlation may represent a constraint in the degree of plasticity that can evolve in tadpoles. Evolution of
higher activity levels when no predator is present (beneficial
because foraging rates increase) will lead to concurrent evolution of higher activity levels in the presence of predators
(detrimental because predation risk goes up). Likewise, the
degree to which tadpoles can reduce activity in the presence
of predators would be limited because some minimum level
of activity when no predator is present is necessary. Previous
research has found that baseline activity level (activity level
when no predator is present) is the best predictor of tadpole
predation risk (Lawler, 1989). That evolution of activity rates
were found to be correlated in this study supports this hypothesis.
This potential constraint in evolution of plasticity has not
prevented species from acquiring some plasticity in behavioral
responses to predators. Tadpoles, in general, decreased the
amount of time spent active in the presence of predators, especially Lepomis. This was true even for those species found
Table 3
Correlations in evolution of time active under four predator
treatments (no predator present, with Anax, with Notophthalmus,
and with Lepomis)
No predator w/ Anax
No predator w/ Notophthalmus
No predator w/ Lepomis
Anax w/ Notophthalmus
Anax w/ Lepomis
Notophthalmus w/ Lepomis
LCL
UCL
0.697
0.229
0.695
0.631
0.702
0.769
0.886
0.767
0.913
0.892
0.926
0.928
Upper and lower confidence limits for all six possible correlations
as determined by randomization tests (UCL and LCL, respectively;
see text for details) are presented. Time active was measured as the
natural logarithm-transformed s active per 900 s observation period.
only in fishless ponds. This may reflect the presence of ancestral traits that selection has not acted against. Or, tadpoles
may respond to a general predator cue that is common to
fish, newt and insect predators. Decreased activity in the presence of predators implies that those species which frequently
encounter predators will suffer reduced growth rates via reduced foraging rates, which are a consequence of decreased
activity levels (Anholt and Werner, 1995; Werner and Anholt,
1993).
Variance among species in activity levels was large relative
to variance between habitats. This implies that even within a
lineage, species can evolve to have different trait values within
the same selective environment. However, a significant family
by predator effect implies some phylogenetic inertia in trait
values. It suggests that change in activity level when predators
are present differs among lineages. This may reflect the fact
that some hylid species greatly increased activity levels in the
presence of some predators, while ranid and bufonid species
did not. Change in activity with predators was significant for
the vernal pond species H. squirella, as expected given the
more variable nature of predator presence in vernal ponds.
However, with no predators present, activity levels in H. squirella were significantly lower than species from other pond
types. This was a surprising result because vernal pond species
must develop quickly and activity levels are generally correlated with growth rates. Other data confirm that H. squirella
do not have an exceptionally high growth rate, but rather
develop and metamorphose at a small size (Richardson, unpublished data).
As expected, ranids in dragonfly ponds (R. clamitans and
R. sylvatica) were more active than most species from fish or
multiple pond types. However, hylids in dragonfly ponds were
not significantly more active than either fish pond species, or
bufonids and ranids in multiple ponds. Similarly, hylids in
multiple ponds (P. crucifer and A. gryllus) were less active than
other species, as expected, but bufonids and the ranid using
multiple ponds were not less active than fish or dragonfly
pond species. It appears that ranids in dragonfly ponds and
hylids in multiple ponds occupy the predicted adaptive peak,
while other species in those pond types have perhaps found
alternative adaptive peaks.
The relative activity levels of species in this study were qualitatively similar to previously published interspecific observations (reviewed in Skelly, 1997). This finding suggests that activity level is a repeatable species-specific trait. For example, a
variety of studies have now revealed P. crucifer to have extremely low activity levels whether a predator is present or not
(Lawler, 1989; Skelly, 1995; this study). In general, tadpoles of
Bufo were among the most active in this study, as has been
Richardson • Activity in tadpoles
observed in European anurans (Laurila et al., 1997). Bufo in
this study also responded weakly to different predator treatments. Some bufonids form cohesive swarms as an anti-predator response (Watt et al., 1997) and thus Bufo appears to use
an anti-predator mechanism other than reducing activity level
(but see Anholt et al., 1996).
Response to predators varied from predictions in some species. Not all species reduced activity in the presence of predators. In the present study, H. squirella and R. catesbeiana both
increased activity levels in the presence of a newt predator.
This is counter to the dogma of prey reducing activity in the
presence of predators, but increased activity in the presence
of a predator has also been observed in some invertebrate
prey; this response could reflect refuge-seeking activity (Lima,
1998; Lima and Dill, 1990). Prey that can escape predators
through swimming (as seems likely for tadpoles with Notophthalmus and Anax predators) will be induced to swim whenever disturbed by a predator. Thus, if predators are active,
prey disturbance will increase, and prey activity levels will increase. This does not require physical contact between predator and prey; the movement of water created by moving predators can induce tadpoles to swim away from the disturbance
(personal observation).
One difficulty inherent in this study is the need to use a
‘‘common garden’’ experiment. Species can only be compared when data are collected under controlled conditions.
Although I tried to ensure that conditions matched the natural situation, there is no way to confirm if behavior in the
lab is identical to behavior in the field. Any measures of behavior in the field are necessarily confounded by difference
in the habitat type in which species are found. Furthermore,
activity levels cannot be measured for many species in the
field; the only examples are of species found in shallow, clear
rock pools (Smith and Van Buskirk, 1995). Species found in
deeper and murkier waters cannot be observed this way. In
addition, these sorts of observations are confounded by observer presence, something that can be avoided in the lab by
using a video camera. For the purposes of this study, it is not
vital that measures of activity rates are quantitatively precise
because we are interested in activity rates of species relative
to one another. Thus, we must assume only that any lab effect
on behavior is similar for all species considered.
This study also did not control for early developmental environment of individuals. Phenotypic plasticity can occur in
traits that are fixed during development, as well as in labile
traits, such as behavior, that change as quickly as the environment (Scheiner, 1993). Phenotypic plasticity in fixed morphological traits has been documented in larvae of several anuran
species (McCollum and Van Buskirk, 1996; Smith and Van
Buskirk, 1995; Van Buskirk et al., 1997; Van Buskirk and Relyea, 1998). Some aspects of behavior may also be fixed
through differences in the environment experienced during
development. Such responses can lead to inter-population differences within a species. Because I could not control for this
variability in developmental environment, I instead randomized for this effect. For each species I collected individuals
from two or more disjunct (⬎ 10 miles apart) populations.
This reduced the chance that data collected might reflect an
aberrant population mean instead of characterizing the species’ mean. Note that ignoring this within species variation
has likely increased ‘‘noise’’ in my data, leading to estimates
of within species variance that will be higher than the true
variance. The presence of species differences that are evident
despite this uncontrolled variation within species reveals that
differences between species surpasses intra-specific population
differences.
In summary, this study finds that tadpoles in general respond to the presence of predators (including those they
57
would not typically encounter) by reducing activity levels. The
study further reveals that despite its lability, activity behavior
displays a consistent pattern among species and thus is a suitable metric for characterizing species’ differences. Finally,
phylogenetic analysis in this study reveals that, at least for tadpoles of the species considered, behavioral response to the
environment may be evolutionarily constrained. This suggests
that behavioral traits may not always be more easily and quickly modified during evolution than morphological or physiological traits, as has been typically assumed (Gittleman et al.,
1996; Wcislo, 1989).
Thanks to the many folks who provided help in the field: Charlie
Baer, Steve Fradkin, Nick Friedenberg, Howard Horne, Lisa Horth,
Mark McPeek, and Tom Waltzcek. And thanks to all who read and
commented on this manuscript: Brad Anholt, Doug Bolger, Carol
Folt, Richard Holmes, Jeff Leips, Mark McPeek, Joe Travis, David
Westneat and two anonymous reviewers. This research was supported
by an Animal Behavior Society Research award to J.M.L.R., an NSF
dissertation improvement grant (IBN-97008777) to Mark A. McPeek
and J.M.L.R. and by NSF grants (DEB-9419318, IBN-97007787) to
Mark A. McPeek.
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