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First published online November 19, 2007
Journal of Experimental Biology 210, 4104-4122 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.007930
Phylogenetic comparative analysis of electric communication signals in ghost knifefishes (Gymnotiformes: Apteronotidae)
1 Department of Biology, Indiana University, Bloomington, IN 47405,
USA
2 Center for the Integrative Study of Animal Behavior (CISAB), Indiana
University, Bloomington, IN 47405, USA
3 CISAB Research Experience for Undergraduates Program, Indiana University,
Bloomington, IN 47405, USA
4 Dominican University, River Forest, IL 60305, USA
5 Laboratório de Fisiologia Comportamental (LFC), Instituto Nacional
de Pesquisas da Amazônia (INPA), Manaus, AM 69083-000, Brazil
6 Smithsonian Institution, National Museum of Natural History, Division of
Fishes, Washington, DC 20560, USA
7 Program in Neuroscience, Indiana University, Bloomington, IN 47405,
USA
* Author for correspondence (e-mail: turnercr{at}gmail.com)
Accepted 30 August 2007
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Key words: communication, behavior, signal evolution, comparative approach, phylogenetic analysis, electric fish, chirping
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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).
Electrocommunication in weakly electric fish is an outstanding model for
integrating mechanistic and historical biology (sensu
Autumn et al., 2002) because
these behaviors are diverse across species, are easily recorded and analyzed,
and are controlled by a well-characterized neural circuit. The family
Apteronotidae is particularly well-suited for a comparative approach because
it has the highest species diversity among Neotropical electric fish
(Crampton and Albert, 2006).
Apteronotids continuously emit a quasi-sinusoidal voltage signal or electric
organ discharge (EOD) that has two functions. Nearby objects locally distort
the electric field generated by the EOD, and by detecting these distortions
with their electroreceptors, fish can electrolocate. The fish can also use
their EODs to communicate by detecting the interactions of their own EOD with
those of other fish. Individual apteronotid fish maintain an extremely stable
EOD frequency (EODf) (Bullock,
1970; Moortgat et al.,
1998) and waveform (Rasnow and
Bower, 1996). In addition to using the baseline EOD frequency and
waveform as communication signals, fish also modulate the frequency and
amplitude of the EOD during social interactions
(Hagedorn and Heiligenberg,
1985; Larimer and MacDonald,
1968). Two categories of EOD modulations (EODMs) are typically
produced: chirps (Bullock,
1969), which have short durations (
10–1000 ms) and
relatively large increases in frequency (50–600+ Hz), and gradual
frequency rises (GFRs), which usually have longer, more variable durations
(10 ms–60 s) and less frequency modulation (<100 Hz)
(Engler et al., 2000).
Comparative studies (>2 species)
(Garland and Adolph, 1994) of
apteronotid electrocommunication have focused primarily on EOD frequency and
waveform because the EOD is constantly emitted and its frequency and waveform
are therefore easily measured (Crampton,
1998; Crampton and Albert,
2006; Heiligenberg and
Bastian, 1980; Hopkins and
Heiligenberg, 1978; Kramer et
al., 1981; Steinbach,
1970). These signals vary across species and are sexually
dimorphic in some species. They can therefore function as broadcast signals
that continuously allow receivers to gain information about the sex and
species of the signaler. EODf and waveform overlap between some sympatric
species, however, which may limit their ability to unambiguously convey
species identity (Crampton,
1998; Crampton and Albert,
2006; Kramer et al.,
1981).
Unlike the EOD, chirps are evoked signals that are emitted primarily during
agonistic encounters and courtship
(Hagedorn and Heiligenberg,
1985). Because chirps are actively produced in response to social
stimuli rather than being continuously emitted, they can convey immediate
motivational and conditional information as well as information about species
identity and sex (Hopkins,
1974b). The function of chirps in active agonistic and
reproductive communication might have exposed them to strong sexual and
natural selection. If so, the structure and production of chirps should have
evolved to be at least as diverse across species as EOD frequency and
waveform. The transience of chirps and GFRs, however, also makes them more
difficult to record than EOD frequency and waveform, and these signals have
been studied in few species. Of the 60+ apteronotid species, chirps and GFRs
have been described in only three (Apteronotus leptorhynchus, Apteronotus
albifrons and Adontosternarchus devenanzii)
(Dunlap and Larkins-Ford,
2003; Engler et al.,
2000; Kolodziejski et al.,
2005; Zhou and Smith,
2006; Zupanc and Maler,
1993). The structure of chirps, and to a lesser extent GFRs,
varies considerably across these three species, but broader patterns of EODM
evolution remain unexplored.
To gain a more complete understanding of the evolution of communication in apteronotids, we described the structure of electrocommunication signals in 10 additional apteronotid species, focusing particularly on chirps and GFRs. We then used discriminant function analysis to characterize species diversity in key signal features. Finally, to look for phylogenetic evidence of mechanistic relationships shaping signal evolution we tested for correlations between several electrocommunication parameters.
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]
were collected from the Rio Solimões/Rio Negro confluence at Ilha do
Catalhão near Manaus, Brazil in May 2005. These subjects were housed in
an aquarium system at the Laboratory of Behavioral Physiology (LFC) in the
Instituto Nacional de Pesquisas da Amazônia in Manaus. Table S1 in
supplementary material summarizes sex and body size data for the subjects.
Fish were housed individually (if aggression was observed) or in groups in
36-liter tanks and were recorded within one week of capture. Subjects from seven species [10 P. hasemani, 10 `Apteronotus' bonapartii (Castelnau 1855), seven P. gimbeli, 10 Adontosternarchus balaenops (Cope 1878), nine Sternarchogiton nattereri (Steindachner 1868), one Sternarchogiton porcinum Eigenmann and Allen 1942, and eight Sternarchorhynchus cf. roseni Mago-Leccia 1994] were obtained through a commercial tropical fish dealer (Ornamental Amazon Fish Aquarium, Iquitos, Peru) in May 2006. The dealer collected the fish from the Amazonas River (P. hasemani, A. bonapartii, P. gimbeli, S. nattereri, A. balaenops) and the Itaya River (S. cf. roseni) near Iquitos. Fish were housed individually (if aggression was observed) or in groups in 36-liter, 64-liter, 210-liter or 340-liter tanks within a recirculating aquarium system at Indiana University. The tanks were maintained on a 12 h:12 h light:dark cycle at 26.0–26.7°C, pH 4.5–6.0, and conductivity of 100–300 µS cm–1. Fish from Peru were recorded within 4 months of receipt from the fish dealer. This study was conducted within the guidelines outlined by the National Institute of Health's `Guide for the Care and Use of Laboratory Animals', and all protocols were approved by the Bloomington Institutional Animal Care and Use Committee (BIACUC) at Indiana University.
To compare the production of communication signals across species, we
re-analyzed previously published data
(Kolodziejski et al., 2005;
Zhou and Smith, 2006) from
three additional species [Apteronotus leptorhynchus Ellis 1912,
Apteronotus albifrons Linnaeus 1766, and Adontosternarchus
devenanzii Mago-Leccia, Lundberg and
Baskin 1985].
Behavioral recordings
Electrocommunication signals were recorded with a stimulus playback design
following the methods of Kolodziejski et al.
(Kolodziejski et al., 2005).
Experiments occurred at various times of day and night and were performed in a
completely darkened recording chamber. Although more EODMs are produced in the
dark phase of the light:dark cycle, experiments with constant light or dark
conditions demonstrate that the production of EODMs is directly responsive to
light condition rather than being under an endogenous circadian rhythm
(Zupanc et al., 2001) (C.R.T.,
unpublished observations). Subjects were placed inside a custom-built PVC tube
with plastic mesh covering both ends and a mesh-covered aperture at the tube's
midpoint. The size of the tube was adjusted for each subject to minimize
movement while allowing normal body position. The tube was placed in the
center of a 37-liter recording aquarium maintained at 25.8–27.0°C
and containing water from the fish's home tank. The behavioral chamber was
then closed and the fish was allowed to acclimate to the recording tank for 30
min. A pair of carbon electrodes placed at the fish's head and tail recorded
its EOD, and a second pair of electrodes on either side of the recording tube
was used to present playback stimuli. The signal from the recording electrodes
was band-pass filtered (0.1 Hz–10 kHz), amplified
[100–1000x; model P-55 (Grass Instruments, West Warwick, RI, USA)
or model 3000 (A-M Systems, Sequim, WA, USA)] and digitized at 44.1 kHz on the
left channel of a sound card in a computer running Cool Edit Pro (Syntrillium,
Phoenix, AZ, USA). Playback stimuli were sinusoidal voltage signals generated
by a function generator (Model AFG320; Sony/Tektronix, Tokyo, Japan) or by a
computer using Cool Edit Pro and were calibrated to a species-specific
root-mean-square (RMS) field amplitude (0.3–1.5 mV
cm–1) measured parallel to the stimulating electrodes and
midway between them. The amplitude was kept the same within each species and
was chosen to mimic the EOD amplitude of that species. A copy of the stimulus
was digitized on the right channel of the sound card. A 4 min baseline
recording was made from each fish without stimulation, and five recordings
were made with different playback stimuli. Each recording consisted of a 1 min
baseline period with no stimulation, two minutes of playback stimulation and 1
min post-stimulus. The frequencies of the playback stimuli were set relative
to each subject's own baseline EOD frequency: 150 Hz above and below the EOD
frequency (±150 Hz), 20 Hz above and below the EOD frequency
(±20 Hz) and 5 Hz below the EOD frequency (–5 Hz). The playback
frequencies spanned the species-typical range of EOD frequencies and were
meant to simulate the presence of a conspecific fish in the recording tank.
The –5 Hz stimulus was expected to evoke a jamming avoidance response
(Bullock et al., 1972). The
stimuli used were the same as those in previous studies of EODMs in A.
leptorhynchus, A. albifrons and A. devenanzii, which allowed us
to compare our results directly with those studies
(Kolodziejski et al., 2005;
Zhou and Smith, 2006). Stimuli
were presented in random order and were separated by 10-min intervals without
stimulation. We pooled measurements of EODMs across all of the playback
stimuli, and our measurements therefore represent the overall signal
production to playbacks across a species-typical range of EOD frequencies.
Because this study focused primarily on species differences in the structure
of signals, cross-species comparisons of chirp and GFR production as a
function of stimulus frequency are beyond the scope of the present study and
will be presented as part of a subsequent study.
Signal parameters
Baseline EOD
The baseline EODf (Fig. 1A)
of each fish was measured during its recording session by using the frequency
analysis function in Cool Edit Pro [fast Fourier transform (FFT) size=65536].
Water temperature was recorded to the nearest 0.1°C, and a
Q10°C of 1.6 was used to correct each EODf measurement to that
expected at 25.0°C (Dunlap et al.,
2000). To quantify one parameter of EOD waveform, we also measured
`waveform complexity' (Fig. 1B)
as the powers of the second and third FFT harmonics relative to that of the
fundamental (F2–F1 and F3–F1, in dB). More positive values of
F2–F1 and F3–F1 indicated more complex (i.e. polyphasic)
waveforms. We used a customized procedure written by Brian Nelson (BSound
version 1; available at
http://homepage.mac.com/bsnelson/Igor/BSound.html)
and running in Igor Pro (Wavemetrics, Lake Oswego, OR, USA) to generate a
power spectrum (8192 points, Hanning window) of the baseline recording of each
fish and to measure the power of the first three harmonics. The power of the
fundamental (in dB) was then subtracted from the power of the second and third
harmonics to quantify the relative power of each harmonic.
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).
Briefly, any playback-induced contamination of the recording was removed by
subtracting an appropriately scaled and phase-shifted copy of the playback
signal. The fundamental frequency of the EOD was calculated by using an
autocorrelation algorithm on 6 ms Hanning windows, advanced 2 ms per
iteration. This process resulted in a temporal resolution of 2 ms and a
frequency resolution of 0.5–3 Hz, depending on the signal–noise
ratio of the recording. The RMS amplitude of the EOD was calculated on the
same 6 ms windows. eFish used the mode of EODf in sliding 2 s windows as a baseline frequency from which to detect EODMs. The procedure counted EODMs as any event in which EODf exceeded this baseline frequency by more than 3 Hz for more than 10 ms and less than 60 s. The beginning and end of each EODM was then defined as the time at which EODf crossed a threshold of 1 Hz above or below the baseline frequency. eFish then recorded the time and EODf at five points on each EODM: positive start, positive peak, positive stop, negative peak, and negative stop (Fig. 1C). For EODMs without undershoots (negative phases), only the first three points were recorded. The RMS amplitude data from each recording were imported into Microsoft Excel (Microsoft Corp., Bellevue, WA, USA), and we measured the maximum and minimum RMS amplitude during the positive phase of each chirp (Fig. 1C). Each EOD modulation was also examined by the experimenter to confirm that the automated procedure accurately identified the EODM and measured its parameters. Using the measurements of time, EODf and EOD amplitude, we calculated the following signal parameters for each EODM: duration, frequency modulation (FM), relative amplitude modulation (%AM), undershoot FM, positive FM slope, and negative FM slope (Table 1).
We differentiated GFRs from chirps in each species by visually examining the positive FM versus duration of all EODMs on a scatter plot (see Results). In all species except S. cf. roseni, A. leptorhynchus, A. albifrons and A. devenanzii, the two categories of EODMs formed discrete clusters distinguishable by the amount of FM. In S. cf. roseni, A. leptorhynchus, A. albifrons and A. devenanzii, the FM and duration of low-FM chirps and GFRs overlapped somewhat, but they could be distinguished based on a combination of FM and duration. Because the FM and the duration of GFRs are positively correlated within species, we used lines based on this FM–duration relationship to distinguish chirps from GFRs. For S. cf. roseni, A. leptorhynchus and A. albifrons, we classified all EODMs having FM>21x(duration)+10 as chirps. In A. devenanzii, GFRs and chirps formed discrete clusters distinguishable by the line FM=21x(duration)+25 (see Results).
In two species (P. hasemani and P. gimbeli), some chirps displayed extreme and prolonged reduction of EOD amplitude (Fig. 1D) (see Results). When EOD amplitude dropped below 15% of its baseline, the ability to resolve EODf became erratic. Therefore, we fixed the estimate of EODf at its last measurable value between the first sampled window in which RMS amplitude dropped below the 15% threshold and the first window in which the RMS amplitude returned to at least 15% of baseline (Fig. 1D). We used the mode of RMS amplitude in sliding 1-s windows as a baseline amplitude from which to detect levels below 15%. Once RMS amplitude returned to at least 15% of baseline, the EODf was calculated as before in eFish.
Values for signal parameters were averaged for each fish, and all statistical and phylogenetic analyses were performed on the individual means.
Discriminant function analysis
We used discriminant function analyses (DFAs) to assess signal diversity
and to quantify the ability of different signals (EODs, chirps and GFRs) to
carry species-identifying information. One of the assumptions of DFA is low
multicolinearity of independent variables [i.e. lack of strong correlations
between variables (Spicer,
2004)]. To ensure that the signal parameters used in the DFA were
independent of each other, we first performed separate principal components
analyses (PCAs) on parameters of EODs, chirps and GFRs. The PCA factors were
then used as independent variables in the DFAs, with species as a grouping
variable. Four separate DFAs were performed: one with all signal variables,
and separate DFAs with only EOD, only chirp and only GFR variables. The
contribution of different signal parameters to species diversity and
discrimination was estimated by the loadings of the signal variables on the
DFA. To assess the ability of different signal types (EODs, chirps and GFRs)
to identify species, we also used a cross-validated classification using the
canonical roots created by the DFAs
(Spicer, 2004). We excluded
one individual of each species from the analysis and predicted the species of
the excluded individuals based on the canonical roots from DFAs generated from
the remaining individuals. This process was repeated with different
individuals excluded until each individual was classified at least once. The
percentage of individuals whose species was correctly classified provided an
index of the ability of the signals in the DFA to characterize species
identity.
Phylogenetic comparative analysis and relationships between signal parameters
To detect phylogenetically independent relationships between signal
parameters, we used a method based on Felsenstein's independent contrasts [FIC
(Felsenstein, 1985)]. The
phylogenetic generalized least squares method [PGLS
(Martins and Hansen, 1997)],
as implemented in the program COMPARE
(Martins, 2004), was used to
test a priori hypotheses about the relationships between these
parameters (see Martins et al.,
2004; Martins and Lamont,
1998; Ord and Martins,
2006). We chose PGLS because it performs well with small
interspecific sample sizes, uses a range of microevolutionary models, and
scales branch lengths by using the comparative data (see below)
(Martins, 1999;
Martins et al., 2002;
Martins and Hansen, 1997).
PGLS has been used previously to investigate the evolution of communication
signals in other vertebrate species
(Martins et al., 2004;
Ord and Martins, 2006;
Ord and Stuart-Fox, 2006).
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)
(Fig. 2B) – is based on a
maximum parsimony analysis of 249 morphological, physiological and behavioral
characters (Albert, 2001) with
minor additions from two single-genus studies
(Campos-da-Paz, 2000;
Mago-Leccia et al., 1985).
Note that although Crampton and Albert
(Crampton and Albert, 2006)
used `Apteronotus' porcinum, here we use the older name,
Sternarchogiton porcinum. Because branch-length estimates were not
available for either phylogeny, we set the total length of each phylogeny
equal to one such that tip species were aligned at the top of the tree
(Fig. 2). Importantly, the PGLS
method in COMPARE uses the comparative data themselves to estimate best-fit
branch lengths and is thus robust to inaccuracy in the initially specified
branch lengths (Martins et al.,
2002).
PGLS uses a single parameter, alpha (
), which can be interpreted as
the fit of the comparative data with a specific evolutionary model. When
=0, phenotypic change (i.e. change in signal parameters) and
phylogenetic distance are linearly related and PGLS behaves identically to
FIC. In this linear model, evolutionary change happens via random
genetic drift or fluctuating directional selection (Brownian motion). When
is greater than zero, phenotypic change and phylogenetic distance are
exponentially related. In this exponential model, evolutionary change happens
via stabilizing selection, and the magnitude of represents
the strength of constraint around a fixed optimum. Thus, when is
extremely large (e.g. 15.5, the maximum value used in COMPARE), the constraint
is extremely large, and phylogenetic effects on trait evolution are
unimportant. PGLS trait regressions using the large are identical to
standard, non-phylogenetic regressions (TIPS).
We used a contrasts approach to examine relationships between different
signal parameters across apteronotid species. Although all possible
relationships between signal parameters could have been evaluated, we limited
our significance tests to those for which we had a priori hypotheses
(see Results). This approach avoids the problems associated with large numbers
of statistical comparisons. For each regression, the PGLS-Relationships module
of COMPARE provided separate results using the TIPS model (=15.5), the
FIC model (=0) and a maximum-likelihood (ML) estimate of . Using
these three different PGLS models allowed us to assess the robustness of
particular results to assumed models of phenotypic evolution. Significance
tests were performed on each of the three results for all regressions, and
thus correlation coefficients are reported as ranges. Following the method
outlined in Martins (Martins,
1996) we tested the significance of correlation coefficients by
developing a 95% confidence interval around the mean regression slope
estimated using the two alternative phylogenies separately. This procedure
addresses potential error due to phylogenetic uncertainty. A correlation
coefficient was considered to be significantly different from zero if the
confidence interval of the mean regression slope did not include zero
(P<0.05).
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;
Kramer et al., 1981). EODfs
ranged from 644 to 1433 Hz across species, with intraspecific ranges typically
spanning 100–300 Hz. Modified biphasic EOD waveforms were common, but
some species (e.g. Sternarchorhynchus spp., S. terminalis
and `A.' n. sp. B) had more complex, triphasic waveforms. Each taxon
produced EODMs that fit into the two existing general categories of chirps and
GFRs, but production rate and structure differed across species. Below, we
highlight particularly novel or interesting aspects of the
electrocommunication signals in each genus. EODMs in A. leptorhynchus, A.
albifrons and A. devenanzii have been described previously
(Dunlap and Larkins-Ford,
2003; Engler et al.,
2000; Kolodziejski et al.,
2005; Zhou and Smith,
2006; Zupanc and Maler,
1993) and are therefore not described in detail here.
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Sternarchorhynchus spp.
Chirps in both Sternarchorhynchus species were produced at
extremely low rates and had FM that only slightly exceeded that of GFRs. The
seven recorded S. cf. curvirostris produced a total of 16
chirps and 65 GFRs. The eight recorded S. cf. roseni
produced a total of 5 chirps and 26 GFRs. Although both
Sternarchorhynchus species had EODs with triphasic waveforms, this
feature was more pronounced in S. cf. roseni, as evidenced
by the greater relative power of the second and third harmonics in this
species (Table 2,
Fig. 3B). EODf was higher in
S. cf. roseni than in its congener, and EODf ranges did not
overlap between the two species.
Parapteronotus hasemani
P. hasemani produced chirps with extraordinarily high frequency
modulation (>500 Hz above baseline) and amplitude modulation (>90%AM).
Chirps with the most pronounced amplitude reduction resulted in a brief,
complete cessation of the EOD. This type of EODM has been commonly observed in
the non-apteronotid genera Sternopygus
(Hopkins, 1974a) and
Eigenmannia (Hagedorn and
Heiligenberg, 1985), where it was termed an `interruption'. Many
chirps also had extremely long durations (>1 s), and the range of chirp
durations was the largest of all species
(Table 3). Low-FM chirps
(<200 Hz above baseline) were rare.
The 10 recorded P. hasemani from Peru produced a total of 1158 chirps and 1404 GFRs in two recording sessions. 711 of the GFRs were a distinct and novel type of EODM that we have named `rasps' because of their sound when transduced into audio form (Fig. 4). Rasps were characterized by a variable-duration sequence of small (<25 Hz) peaks in EODf. The EODf peaks were generally highest in the middle of a rasp. Rasps were produced both spontaneously and during playback stimulus. The four recorded P. hasemani from Brazil produced a total of 363 chirps and 94 GFRs. Chirps from the Brazilian population were almost exclusively interruptions (90% of chirps had >90%AM), and none of the GFRs resembled rasps. These differences suggest that the structure of electrocommunication signals may vary not only across species but also across populations of the same species.
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Sternarchogiton spp.
The nine recorded S. nattereri produced a total of 1107 chirps and
112 GFRs. Although a few chirps had FM as high as 500 Hz, most fell between 50
and 300 Hz. Of all the species measured, S. nattereri had the
broadest range of EODf (404 Hz) (Table
2). The one recorded S. porcinum produced 217 chirps and
eight GFRs. The chirps produced by this individual were similar in structure
to the lower-FM chirps produced by S. nattereri.
Porotergus gimbeli
The seven recorded P. gimbeli from Peru produced a total of 27
chirps and 26 GFRs. The two recorded P. gimbeli from Brazil produced
a total of 20 chirps and 22 GFRs. Chirps from the Brazilian population
exhibited greater AM than those from the Peruvian population. Also, the
Brazilian P. gimbeli chirps, unlike those from the Peruvian
population, nearly always ended with a small frequency undershoot
(Table 3). P. gimbeli
from Peru produced only two chirps with more than 70%AM, whereas most of the
chirps produced by the Brazilian population were interruptions (>90%AM).
EOD waveform was more complex in P. gimbeli from Brazil, and fish
from this population also had lower baseline EOD frequencies than all of the
P. gimbeli from Peru (Table
2, Fig. 3B). As
with P. hasemani, these differences suggest population-level
divergence in electrocommunication signals.
Sternarchella terminalis
The five recorded S. terminalis produced a total of 2308 chirps
and 163 GFRs. S. terminalis produced chirps at a very high rate and
in a novel `burst-like' fashion, in which trains of very short duration chirps
occurred on top of a slightly elevated baseline EODf
(Fig. 5). Occasionally, these
short-duration chirps occurred in extremely rapid succession to form
long-duration, multi-peaked chirps (Fig.
3D). Most S. terminalis chirps fell within a
comparatively narrow range of duration (0.007–0.04 s) but a broad FM
range (50–300 Hz above baseline).
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) were
never seen in A. balaenops. The EOD waveform of A. balaenops
was more complex than that of A. devenanzii
(Table 2) due to a prolonged
`shoulder' where voltage transitioned from the negative to positive phase
(Fig. 3B). A.
balaenops also had a lower baseline EODf than A. devenanzii.
Thus, the two species of Adontosternarchus provide another example in
which the electrocommunication signals of closely related species have
diverged significantly.
Discriminant function analyses
Separate principal component analyses on EOD, chirp and GFR parameters were
used to generate independent variables for DFA. The first factor of the PCA on
EOD parameters was loaded primarily by EOD waveform (F2–F1 and
F3–F1) and accounted for over 60% of the variance
(Table 5). EOD frequency loaded
robustly on the second factor, which accounted for most of the remaining
variance. The chirp PCA was more complex, with chirp %AM, FM, duration and +
FM slope all loading heavily on the first factor, –FM slope on the
second factor, and undershoot FM on the third factor. Together, these three
factors explained more than 90% of the variance. The PCA on GFR parameters was
dominated by two factors that were strongly influenced by all four structural
parameters (duration, FM, and + and –FM slopes).
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The DFA using all of the factors from the EOD, chirp and GFR PCAs revealed strong influences of EOD and chirp parameters, but not GFR parameters, on interspecific signal variation. The first two chirp factors and the first two EOD factors were by far the strongest contributors to the discriminant model, whereas the GFR parameters contributed the least to the model (Table S2 in supplementary material). Both EOD and chirp variables were highly correlated with the first three canonical roots of the DFA, which explained over 80% of the variance in the model, whereas GFR variables were poorly correlated with these roots (Table 6). The DFA model based on the combined EOD, chirp and GFR parameters was largely successful at segregating species based on these signals, although there was still overlap between some species (Fig. 6A,B). This was also revealed by the fact that the DFA using all signal parameters correctly classified species identity 78.3% of the time in leave-one-out cross-validations, which is far greater than the 9.1% expected based on chance alone but still less than 100% (Fig. 6C).
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Separate DFAs on EOD parameters only, chirp parameters only, and GFR parameters only confirmed that EOD and chirp parameters varied more across species and are much stronger predictors of species identity than GFR parameters. Cross-validated classifications based on EOD- or chirp-based DFAs correctly identified the species of 63.5% and 67.5% of individuals, respectively, whereas DFAs based on GFR parameters correctly identified the species of only 28.9% of individuals (Fig. 6C).
Relationships between signal parameters
Our a priori hypotheses were based on aspects of the neuroanatomy
and neurophysiology of the electromotor circuit in A. leptorhynchus
(reviewed in Smith, 1999),
many aspects of which are likely to be conserved across apteronotids. EODf in
this species is controlled by pacemaker neurons in the medullary pacemaker
nucleus (Pn). Relay cells in the Pn convey this command signal to electromotor
neurons in the spinal cord, whose axons form the electric organ. Firing rates
of pacemaker, relay and electromotor neurons correspond directly to EODf.
EODMs, including chirps and GFRs, are caused by glutamatergic excitatory
inputs to the Pn from the thalamic prepacemaker nucleus (PPn) and midbrain
sublemniscal prepacemaker nucleus (SPPn) (reviewed in
Heiligenberg et al., 1996;
Metzner, 1999;
Zakon et al., 2002).
First, we asked whether EODf and waveform complexity (as assessed by the
relative strength of the second and third harmonics) were positively
correlated. In some gymnotiform species, waveform complexity is partly due to
rostral and caudal portions of the electric organ firing slightly out of
phase. This asynchrony results from small differences in the conduction time
of the command signal from the Pn (reviewed in
Caputi, 1999). If a similar
mechanism contributes to EOD waveform in apteronotids, then species with
higher EODf may have more complex waveforms. This might occur because fixed
rostro-caudal delays in the conduction time would cause larger rostro-caudal
phase differences in EODs with shorter periods. Alternatively, conduction
delays could change with EODf or waveforms could be determined primarily by
the pathway that electromotor axons follow in the electric organ
(Bennett, 1970;
Bennett, 1971). In this case,
EOD waveform and EODf would be uncorrelated. EODf and waveform complexity were
not significantly correlated across species (r=–0.33 to 0.26,
P>0.05) (Table 7),
which suggests that high-frequency EODs do not necessarily result in more
complex EOD waveforms.
|
Second, we tested the hypothesis that species whose chirps had steeper
returns to baseline EODf also produced chirps with larger frequency
undershoots. Engler et al. proposed that undershoots in A.
leptorhynchus resulted from rapid removal of the PPn-generated excitation
of the Pn at the end of chirps (Engler et
al., 2000). The frequency of the command signal for the EOD is
regulated by sodium and potassium currents in pacemaker and electromotor
neurons (Dye, 1991;
Smith and Zakon, 2000;
Smith, 2006). Frequency
undershoots might result if strong, PPn-mediated depolarization of the
pacemaker network during chirps caused steady-state sodium channel
inactivation that recovered more slowly than the removal of the excitatory
input from the PPn. This hypothesis predicts that more rapid deactivation of
this excitatory input (i.e. steeper –FM slope) should produce more
pronounced undershoots. Consistent with this prediction, undershoots are
absent in two species [A. albifrons and A. devenanzii
(Dunlap and Larkins-Ford,
2003; Kolodziejski et al.,
2005; Zhou and Smith,
2006)], that produce longer duration chirps with shallow –FM
slopes. A significant positive correlation between –FM slope and
undershoot FM of chirps was observed for the TIPS model, but this trend did
not reach significance with the other two models (r=0.22–0.49,
P<0.05 for TIPS; P=0.0505 for ML)
(Table 7,
Fig. 7A). This result therefore
only partly supports the hypothesis that, across apteronotid species, the
rapid removal of excitation needed to produce short-duration chirps is
mechanistically linked to the production of frequency undershoots.
|
Third, we hypothesized that species with higher baseline EOD frequencies
would produce chirps with less FM. The electric organ and neurons in the
pacemaker nucleus fire at rates unsurpassed by cells in any other organism
(Moortgat et al., 1998;
Smith, 1999). If these rapid
firing rates approach an absolute physiological ceiling in species with the
highest baseline EOD frequencies, they might constrain the magnitude of
frequency increases during chirping. EODf and chirp FM were not significantly
correlated (r=–0.2 to 0.1, P>0.05)
(Table 7). This suggests that
an absolute physiological ceiling on neuronal firing rates has not constrained
the evolution of FM in apteronotid chirps, even in species in which EODf
surpasses 2000 Hz during chirps.
Fourth, we asked whether chirp %AM was correlated with chirp FM or with
chirp positive peak frequency. Chirps in all three of the apteronotid species
studied previously have some AM (Dunlap
and Larkins-Ford, 2003; Zhou
and Smith, 2006; Zupanc and
Maler, 1993). Although AM has only been quantified in A.
leptorhynchus (Engler et al.,
2000; Zupanc and Maler,
1993), high levels of AM are associated with high-FM chirps in
this species and in A. albifrons
(Kolodziejski et al., 2005).
Amplitude modulation may result from the inability of neurons in the
electromotor circuit to fire synchronously and/or produce large amplitude
action potentials (APs) when firing at high frequencies. However, a constraint
on EOD amplitude at extremely high frequencies could be relative or absolute.
If the constraint were relative, an upper limit to how fast the neurons could
fire without a reduction in synchrony or AP amplitude would increase as a
species' baseline EODf increased. Consequently, AM would increase as the
amount of FM above the baseline EODf increased, and chirp AM would be
positively correlated with chirp FM across species. By contrast, if the
constraint were absolute, a fixed upper limit to how fast the neurons in the
electromotor circuit could fire without a reduction in synchrony or AP
amplitude would be independent of individual or species-specific baseline
EODf. In this scenario, AM would increase as EODf approached the fixed upper
limit rather than being determined by the amount of change in EODf.
Consequently, chirp AM would be positively correlated across species with
chirp positive peak frequency but not necessarily with chirp FM. We found that
chirp AM and FM were positively correlated (r=0.7–0.89,
P<0.05) (Table 7,
Fig. 7B) but that chirp AM and
positive peak frequency were not (r=0.17–0.26,
P>0.05) (Table 7).
Thus, chirps that had large increases in frequency above the baseline EODf
were likely to result in more AM, independent of the baseline EODf itself.
This result supports the hypothesis that a relative constraint on how fast
neurons in the electromotor circuit can fire without a reduction in synchrony
or AP amplitude shapes the evolution of chirp structure in apteronotids.
|
Discussion |
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|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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Novel signal types
Several novel signal types were produced by some of the species in this
study. One was the bursting of chirps on an elevated baseline EOD frequency
produced by S. terminalis. The chirp bursts of S. terminalis
contrast with the more uniform timing of chirps in response to playback
stimulation in A. leptorhynchus
(Engler et al., 2000;
Zupanc and Maler, 1993). By
producing chirps in bursts, S. terminalis may create an additional
element of signal complexity that could open up new communication channels.
For example, information about motivation or social status might be encoded in
burst duration, interchirp intervals within bursts, or timing between bursts.
Chirp bursts could also contribute to interactive chirping. A.
leptorhynchus produces chirps in an `echo response' pattern during
interactions (Zupanc et al.,
2006). Chirp bursts might similarly provide a mechanism for
interacting S. terminalis to exchange `packets' of chirps and prevent
the overlap of chirps of different fish. To test these hypotheses, more
information is needed about the social ecology and electrosensory physiology
of S. terminalis.
The rasps produced by P. hasemani from Peru are another novel type of EODM. Rasps were relatively common and were produced by all fish from this population but were never produced by the Brazilian P. hasemani. Although the FM and duration of rasps were similar to those of GFRs, the structure of rasps was unlike that of chirps or GFRs in other species. It is possible that rasps have been recorded in other species but not identified above background recording noise because of their low and erratic FM. Indeed, changes in EODf that resemble rasps have been observed during playback stimulation in A. leptorhynchus and A. albifrons but could not be distinguished confidently from recording artifacts (J. A. Kolodziejski, personal communication). Our recordings and playback removal algorithm provided very low levels of background noise and thus allowed greater resolution of low-FM EODMs than in previous studies. Rasps did not coincide with fluctuations in EOD amplitude that occur during fish movement, and they were sometimes produced spontaneously without playback stimulation. These features allowed us to conclusively identify them and measure their parameters. Further studies are needed to determine the function of rasps, their evolutionary history and their mechanisms of production.
Finally, P. hasemani and the Brazilian P. gimbeli
produced chirps that resulted in complete interruptions of the EOD. Although
EOD interruptions are common in the non-apteronotid knifefish
Eigenmannia and Sternopygyus and have similar functions as
chirps (Hagedorn and Heiligenberg,
1985; Hopkins,
1974a; Hopkins,
1974b), they have not been reported previously in apteronotid
species. The closest approximations are extremely rare chirps (e.g. two of
4116 spontaneous chirps) in A. leptorhynchus in which EOD amplitude
was reduced by approximately 80–90%
(Engler et al., 2000;
Heiligenberg et al., 1996;
Zupanc and Maler, 1993).
However, amplitude reduction beyond 90%, as was common in the chirps of P.
hasemani, has not been described in A. leptorhynchus. Thus,
although it is possible that other apteronotids can produce complete
interruptions, only P. hasemani and P. gimbeli use them
extensively. Two possible mechanisms might cause these EOD interruptions. One
possibility is that the excitatory input from the subdivision of the PPn that
controls chirps, the PPn-C, to the Pn is particularly strong in P.
hasemani and P. gimbeli. Such extreme excitation might cause
both the large increase in EODf during the chirp and prolonged depolarization
and inactivation of ion channels that leads to an EOD interruption.
Alternatively, interruptions in these species might be controlled by an
excitatory input to the Pn from the SPPn in the midbrain. In A.
leptorhynchus, current injection into the SPPn caused large increases in
EODf and reductions in EOD amplitude that resembled the rare chirps with
extreme AM and FM in A. leptorhynchus and the interruptions we
observed in P. hasemani and P. gimbeli
(Heiligenberg et al., 1996).
The potential role of the SPPn in high-AM chirps in A. leptorhynchus
has not been studied further, however, because the behavior is so rare.
Because interruptions are common in P. hasemani, this species may
provide a better opportunity to identify the premotor nucleus that controls
these signals.
Species diversity and evolution of EOD signals
Discriminant function analysis indicated that EODs and chirps differed
markedly across apteronotid species whereas the structure of GFRs was much
less species-specific. EOD and chirp parameters were far stronger contributors
to the DFA model than GFR parameters, and DFAs based on EODs or chirps alone
more accurately classified the species of individuals than GFR-based DFAs.
These results demonstrate that EODs and chirps, but not GFRs, can serve as
species-identifying signals. They also suggest more interspecific variation
and evolutionary lability in EODs and chirps than in GFRs. The evolutionary
lability of chirps and EODs is also supported by the variability of these
signals across closely related species and different populations of the same
species (e.g. differences in EODf and chirp structure between A.
balaenops and A. devenanzii, EOD waveform differences between
`A.' bonapartii and `A.' n. sp. B, and the
production of interruptions by Peruvian but not Brazilian P.
gimbeli).
EOD frequency and waveform complexity were strongly correlated with the first canonical root of the DFA, demonstrating that they reliably vary across species. All of the chirp parameters also contributed strongly to the DFA, confirming that chirps, just like EODs, vary substantially across apteronotid species. Three chirp parameters in particular – duration, FM and %AM – strongly influenced the DFA. The most important variable contributing to the DFA model was the first chirp factor, which was loaded primarily by FM, %AM and duration; and these variables also correlated highly with the first two canonical roots of the DFA. This suggests that chirp duration, FM and %AM, like EOD frequency and waveform, are particularly capable of conveying species-identifying information and have undergone substantial evolutionary changes within the Apteronotidae.
Relationships between signal parameters
Testing relationships between signal parameters allowed us to look for
phylogenetic evidence that conserved production mechanisms shape the evolution
of electrocommunication signals. We hypothesized that as EODf increased,
constraints on the conduction velocity of relay axons in the spinal cord might
lead to increased rostro-caudal phase delays in the firing of the electric
organ and increased waveform complexity. Both the independent loadings of EODf
and waveform on the PCA (Table
5) and the lack of significant PGLS correlations
(Table 7), however, revealed
that waveform complexity was not related to EODf. Thus, either EOD waveform is
influenced primarily by trajectory of the electromotor axons, rather than
rostro-caudal phase delays, or conduction velocity is not constraining and can
change to allow EODf and waveform to evolve independently.
Our results partially support the hypothesis that the rate at which
excitation is removed from the Pn at the end of chirps (–FM slope)
influences the evolution of chirp undershoots
(Engler et al., 2000). The
positive correlation between the –FM slope and undershoot FM of chirps
was significant for the TIPS (large , non-phylogenetic) model and
closely approached significance with the ML model but was not
significant for the FIC model (Table
7). Thus, although the rapid deactivation of excitatory input that
causes steep –FM slopes may contribute to the evolution of chirp
undershoots, this linkage is not robust across the phylogeny. For example,
chirps in A. balaenops have comparatively steep –FM slopes but
no undershoot whatsoever (Table
3) (symbol O in Fig.
7A). In this species, adaptations of the electromotor circuit,
such as differences in channel inactivation kinetics in the pacemaker neurons,
might allow a smooth but rapid return to baseline EODf at the end of
chirps.
We found no support for the hypothesis that the amount of FM in chirps was
constrained by baseline EOD frequency. For example, the species with the
highest EODf (`A.' bonapartii) still routinely produced
chirps with up to 600 Hz of FM. This result suggests that as higher baseline
EODfs evolved, the ability to transiently raise EODf to even higher levels
during chirps was retained. This is particularly remarkable given that in
species with EODfs that approach 2 kHz, the neurons that control the electric
organ are producing action potentials at those frequencies. We were unable,
however, to obtain individuals from the species with the highest reported
baseline EODf (Sternarchella schotti; up to 2179 Hz)
(Crampton and Albert, 2006),
and it would be interesting to determine whether these `extremists' are still
able to increase EODf by hundreds of Hz during chirps.
In contrast to the lack of correlation between EODf and Chirp FM, we did
find a strong relationship between chirp FM and EOD amplitude during chirps.
Species whose chirps had greater FM also produced chirps in which amplitude
decreased more (i.e. greater chirp %AM). Indeed, this was the strongest
correlation between any of the measured signal parameters, suggesting that
chirp AM and FM are linked across taxa by a relatively invariant physiological
mechanism. Thus, the extremely high neuronal firing rates necessary to produce
high EODfs have not limited the evolution of chirp FM. Instead, a transient
drop in EOD amplitude is a necessary trade-off incurred by high-FM chirps. The
interspecific trade-off between FM and EOD amplitude during chirps is
paralleled by similar correlations within each species and within individuals
(Engler et al., 2000) (C.R.T.,
unpublished observations). The consistency of this relationship at multiple
levels (across species, individual fish and individual chirps) indicates that
chirp FM and AM are physiologically linked rather than being generated
independently and co-selected. The conserved mechanism producing the tradeoff
is most likely an inability of pacemaker, relay and/or electromotor neurons to
fire synchronously and/or produce large amplitude APs when firing at
frequencies that greatly exceed their baseline firing rates.
Evolution of signal production mechanisms
Patterns in the species diversity and evolution of communication signals
can direct research on the function and production mechanisms of these signals
towards fertile ground. Signals or signal parameters with greater evolutionary
lability may indicate which underlying mechanisms have evolved to produce
signal diversity across species (Emerson,
1996; Nishikawa,
1997).
Species diversity in chirp structure and EOD frequency and waveform
suggests that mechanisms controlling these signals have evolved rapidly. The
electromotor circuit is well-characterized in A. leptorhynchus
(reviewed in Heiligenberg et al.,
1996; Smith,
1999), but studies in other apteronotid species are needed to
understand how the physiology of signal production co-evolves with signal
structure. EOD waveform, which is one of the most evolutionarily labile
signals, has been correlated across a few apteronotid species with the
trajectory of axons in the electric organ. These axons have both rostral- and
caudal-running segments in the electric organ of A. albifrons, which
produces a biphasic EOD waveform, but they run only caudally in
Sternarchorhamphus, which has a monophasic waveform
(Bennett, 1970). Rostro-caudal
asynchrony of electric organ firing occurs in some apteronotids and may also
influence waveform as it does in some species that produce pulse-type EODs
(Caputi, 1999;
Rasnow et al., 1993). Studying
electric organ morphology and physiology in species with complex EOD waveforms
(e.g. S. cf. roseni and S. terminalis)
(Fig. 3B) would provide a
stronger test of these hypothesized mechanisms.
Comparative studies of the pacemaker and prepacemaker nuclei could reveal
the mechanisms of species diversity in EOD frequency and modulations. EODf is
controlled by spontaneous, high-frequency firing of pacemaker neurons in the
Pn (Meyer, 1984). Species
differences in EODf are likely to have evolved through changes in the
physiology of these neurons, including properties of sodium and potassium
currents (Smith, 1999). These
properties, however, have been studied only in A. leptorhynchus
(Dye, 1991;
Smith and Zakon, 2000), and
this hypothesis needs to be tested further by characterizing neuronal
physiology in the Pn of other apteronotid species.
The only documented interspecific variation in the central electromotor
system of apteronotids is a difference between A. leptorhynchus and
A. albifrons in synaptic inputs to the Pn. In A.
leptorhynchus, dendrites of pacemaker and relay cells receive extensive
chemical synaptic input, whereas these dendrites are nearly absent in A.
albifrons (Elekes and Szabo,
1985). Because the synapses on these dendrites are from the
prepacemaker nuclei (PPn and SPPn), which control EODMs
(Dye et al., 1989),
differences in dendritic morphology and synaptic input may contribute to
species diversity in the structure and production of EODMs, including
chirps.
Species diversity in chirp structure, including the novel chirp types found
in this study, warrant comparative studies of the PPn-C and its targets in the
Pn. Of particular interest are mechanisms regulating chirp duration, AM and
FM, which contributed strongly to the species-specificity of chirps. Dunlap
and Larkins-Ford (Dunlap and Larkins-Ford,
2003) hypothesized that the nearly 10-fold difference in chirp
duration between A. albifrons and A. leptorhynchus could
result from species differences in whether glutamate from the PPn-C acted on
NMDA or non-NMDA receptors in the Pn. Interspecific variation in chirp AM and
FM (e.g. less than 100 Hz of FM and little AM in Sternarchorhynchus
spp. versus more than 500 Hz of FM and extreme AM in P.
hasemani) could result from species differences in the robustness of
PPn-C to Pn projections, in the recruitment of PPn-C projection neurons, or in
the strength of post-synaptic responses of Pn neurons. Similarly, species
diversity in the spectro-temporal structure and timing of chirps, such as the
dual-peaked chirps of `Apteronotus' spp., the multi-peaked chirps of
A. devenanzii and the chirp bursts of S. terminalis, may
result from differences in the excitability and/or coupling of PPn-C
projection neurons. Specifically, PPn-C neurons may fire single, synchronous
action potentials in species that produce single-peaked chirps, but doublets,
multiple spikes or spike bursts in species that produce dual- or multi-peaked
chirps or chirp bursts.
These hypotheses are testable with comparative studies because the
electromotor circuit is relatively simple and accessible. A direct
correspondence between the firing rates of neurons in the Pn and EODf and
between the firing of PPn-C projection neurons and chirping means that
diversity in the neuronal physiology can be readily related to behavioral
diversity (Meyer, 1984;
Schaefer and Zakon, 1996;
Kawasaki et al., 1988). These
neurons can also be recorded electrophysiologically both in vivo and
in vitro, which allows studies of intrinsic excitability and synaptic
connectivity (Dye, 1991;
Kawasaki et al., 1998; Heiligenberg et
al., 1996) (J. A. Kolodziejski and G.T.S., unpublished
observations). The anatomy of the Pn and PPn, including cell types, synaptic
inputs and the expression of neuromodulators, is also well-studied
(Ellis and Szabo, 1980;
Elekes and Szabo, 1985;
Kawasaki et al., 1988;
Zupanc and Maler, 1997;
Heiligenberg et al., 1996;
Smith et al., 2000;
Kolodziejski et al., 2005).
Thus, comparative studies of the Pn and PPn will be able to link the evolution
of species diversity in the anatomy and physiology of the electromotor system
to species differences in electrocommunication signals.
Function and perception of diverse signals
Just as species diversity in signal parameters can suggest how signal
production mechanisms evolved, this diversity can also indicate which signal
functions have been subjected to strong directional or disruptive selection
(Cocroft and Ryan, 1995). One
of the main findings of this study was that properties of EODs and chirps were
much more species-specific than those of GFRs. This result raises the question
of why EODs and chirps have evolved so much whereas GFRs have remained largely
conserved. One possibility is that the signal functions of EODs and chirps
have exposed them to strong natural or sexual selection. Both EOD frequency
and chirping are sexually dimorphic in some species and function as signals
used in courtship and/or intrasexual aggression
(Dunlap et al., 1998;
Hagedorn and Heiligenberg,
1985; Kolodziejski et al.,
2005). By contrast, GFRs are not sexually dimorphic, and their
function is more controversial. They have variously been postulated to be
signals of dominance, signals of subordinance, `victory cries' or not to be
communication signals at all (Dye,
1987; Hopkins,
1974b; Kolodziejski et al.,
2007; Serrano-Fernandez,
2003; Tallarovic and Zakon,
2002; Triefenbach and Zakon,
2003). If chirps and EODs, but not GFRs, are used to assess mates
and same-sex rivals, particularly if that assessment includes species
recognition, these signals may be subject to strong sexual selection and
evolve more rapidly than GFRs. Similar examples have been reported in other
taxa. Evolutionary conservation of call structure in Atelopus frogs,
for example, may result from the reduced importance of acoustic signals
relative to visual signals in mate choice in this genus
(Cocroft et al., 1990).
Similarly, song components that are likely to be used in mate choice are
evolutionarily labile in oropendolas, whereas other song components that are
less likely to be mate assessment signals are conserved across species
(Price and Lanyon, 2002).
EOD frequency and waveform varied substantially across apteronotid species,
but were uncorrelated with each other. This suggests that these two signal
parameters evolved independently. The independent evolution of EODf and
waveform may effectively increase the signal space of the EOD and allow more
species-distinctive EODs to evolve. Indeed, substantial overlap of EODf
between sympatric and even syntopic apteronotid species suggests that EODf
alone is not a particularly effective species recognition cue
(Crampton and Albert, 2006;
Kramer et al., 1981).
Combinatorial variations of EODf and waveform may increase the utility of the
EOD as a potential species identification signal, as is supported by the
ability of a DFA based on EODf and waveform to classify species at rates that
far exceed chance (Fig.
6C).
By contrast, the AM and FM of chirps were tightly linked to each other both
within and across species. Although this linkage reflects constraints on the
mechanisms of chirp production (see above), it also has important implications
for the function and perception of chirps. The association between AM and FM
creates redundancy in the signal value of these parameters and may thus
constrain them to convey similar information. For example, in A.
leptorhynchus, high-FM chirps that are used as courtship signals also
have much AM, and chirps with less FM that are used in same-sex interactions
have little AM (Bastian et al.,
2001; Engler et al.,
2000). That redundancy may extend to the electrosensory mechanisms
used to detect chirp AM and FM. Chirps are encoded by P-type electroreceptors
based on the perturbations they produce in the beat pattern of the interacting
fishes' EODs (Benda et al.,
2006). These beat perturbations are a product of the relative
frequencies of the two EODs as well as the AM, FM and duration of the chirp.
Additional studies could test the hypothesis that the potential redundancy of
AM and FM in chirps may function in signal fidelity. For example, could
correlated AM and FM in chirps help the electrosensory system decode them when
they occur in complex social environments, such as when the EODs of multiple
nearby fish interact to produce complex beat patterns?
Conclusions and future directions
The remarkable species diversity in apteronotid electrocommunication
signals raises fascinating questions on their functions and mechanistic
control. A more thorough understanding of the information conveyed by these
signals, their function and social contexts, and the ability of receivers to
detect them is needed to provide a clear picture of how this diversity
evolved. For example, the evolution of EOD waveform in non-apteronotid
gymnotiform fishes has been influenced by selection to avoid electroreceptive
predators (Stoddard, 1999).
Selective pressures contributing to diversity in EOD frequency, waveform and
modulations in apteronotids are less well known. Although it is tempting to
speculate that the evolution of `extreme' chirps with extensive AM and FM,
such as those that occur in P. hasemani, evolved through sexual
selection on males to produce more conspicuous signals, comparative studies on
chirp function are lacking. Indeed, the little comparative evidence to date
suggests that chirp function may itself be evolutionarily labile. Chirp types
with comparable structures in A. leptorhynchus and A.
albifrons are produced in different social contexts and may have evolved
distinct functions (Kolodziejski et al.,
2007). Thus, to understand why some species produce chirps with
more than 500 Hz of FM whereas others produce chirps with less than 100 Hz of
FM, more comparative studies are needed on both the social contexts in which
chirps are produced and how conspecific receivers respond to them. The
evolution of signal structure and function is also likely to be linked to the
evolution of electrosensory systems. Comparing the abilities of the
electrosensory systems of different apteronotids to encode different types of
electrocommunication signals will test the hypothesis that electrosensory
systems are tuned to the complex structure of conspecific signals and provide
a powerful model for examining the co-evolution of signal production and
sensory systems.
List of abbreviations
|
Acknowledgments |
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|
Footnotes |
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|
References |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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Albert, J. S. (2001). Species diversity and phylogenetic systematics of American knifefishes (Gymnotiformes, Teleostei). Misc. Pub. Mus. Zool. Univ. Mich. 190, 1-127.
Autumn, K., Ryan, M. J. and Wake, D. B. (2002). Integrating historical and mechanistic biology enhances the study of adaptation. Q. Rev. Biol. 77,383 -408.[CrossRef][Medline]
Bastian, J., Schniederjan, S. and Nguyenkim, J.
(2001). Arginine vasotocin modulates a sexually dimorphic
communication behavior in the weakly electric fish Apteronotus
leptorhynchus. J. Exp. Bio.
204,1909
-1924.
Benda, J., Longtin, A. and Maler, L. (2006). A synchronization-desynchronization code for natural communication signals. Neuron 52,347 -358.[CrossRef][Medline]
Bennett, M. V. L. (1970). Comparative physiology: electric organs. Annu. Rev. Physiol. 32,471 -528.[CrossRef][Medline]
Bennett, M. V. L. (1971). Electric organs. In Fish Physiology – Sensory Systems and Electric Organs. Vol. 5 (ed. J. S. Hoar and D. J. Randall), pp. 347-391. New York: Academic Press.
Bullock, T. H. (1969). Species differences in effect of electroreceptor input on electric organ pacemakers and other aspects of behavior in electric fish. Brain Behav. Evol. 2, 85-118.[CrossRef]
Bullock, T. H. (1970). Reliability of neurons. J. Gen. Physiol. 55,565 -584.[Abstract]
Bullock, T. H., Hamstra, R. H. and Scheich, H. (1972). The jamming avoidance response of high frequency electric fish. I. General features. J. Comp. Physiol. 77, 1-22.[CrossRef]
Campos-da-Paz, R. (2000). On Sternarchorhynchus castelnau: a South American electric knifefish, with descriptions of two new species (Ostariophysi: Gymnotiformes: Apteronotidae). Copeia 2000,521 -535.[CrossRef]
Caputi, A. A. (1999). The electric organ discharge of pulse gymnotiforms: the transformation of a simple impulse into a complex spatiotemporal electromotor pattern. J. Exp. Biol. 202,1229 -1241.[Abstract]
Cocroft, R. B. and Ryan, M. J. (1995). Patterns of advertisement call evolution in toads and chorus frogs. Anim. Behav. 49,283 -303.[CrossRef]
Cocroft, R. B., Mcdiarmid, R. W., Jaslow, A. P. and Ruizcarranza, P. M. (1990). Vocalizations of eight species of Atelopus (Anura, Bufonidae) with comments on communication in the genus. Copeia 1990,631 -643.[CrossRef]
Crampton, W. G. R. (1998). Electric signal design and habitat preferences in a species rich assemblage of gymnotiform fishes from the upper Amazon basin. An. Acad. Bras. Cienc. 70,805 -847.
Crampton, W. G. R. and Albert, J. S. (2006). Evolution of electric signal diversity in gymnotiform fishes. In Communication in Fishes. Vol. 2 (ed. F. Ladich, S. P. Collin, P. Moller and B. G. Kapoor), pp.641 -725. Enfield, NH: Science Publishers.
Dunlap, K. D. and Larkins-Ford, J. (2003). Diversity in the structure of electrocommunication signals within a genus of electric fish, Apteronotus. J. Comp. Physiol. A 189,153 -161.[Medline]
Dunlap, K. D., Thomas, P. and Zakon, H. H. (1998). Diversity of sexual dimorphism in electrocommunication signals and its androgen regulation in a genus of electric fish, Apteronotus. J. Comp. Physiol. A 183, 77-86.[CrossRef][Medline]
Dunlap, K. D., Smith, G. T. and Yekta, A. (2000). Temperature dependence of electrocommunication signals and their underlying neural rhythms in the weakly electric fish, Apteronotus leptorhynchus. Brain Behav. Evol. 55,152 -162.[CrossRef][Medline]
Dye, J. (1987). Dynamics and stimulus-dependence of pacemaker control during behavioral modulations in the weakly electric fish, Apteronotus. J. Comp. Physiol. A 161,175 -185.[CrossRef][Medline]
Dye, J. (1991). Ionic and synaptic mechanisms underlying a brainstem oscillator: an in vitro study of the pacemaker nucleus of Apteronotus. J. Comp. Physiol. A 168,521 -532.[CrossRef][Medline]
Dye, J., Heiligenberg, W., Keller, C. H. and Kawasaki, M.
(1989). Different classes of glutamate receptors mediate distinct
behaviors in a single brainstem nucleus. Proc. Natl. Acad. Sci.
USA 86,8993
-8997.
Elekes, K. and Szabo, T. (1985). Synaptology of the medullary command (pacemaker) nucleus of the weakly electric fish (Apteronotus leptorhynchus) with particular reference to comparative aspects. Exp. Brain Res. 60,509 -520.[Medline]
Ellis, D. B. and Szabo, T. (1980). Identification of different cell types in the command (pacemaker) nucleus of several gymnotiform species by retrograde transport of horseradish peroxidase. Neuroscience 5,1917 -1929.[CrossRef][Medline]
Emerson, S. B. (1996). Phylogenies and
physiological processes – the evolution of sexual dimorphism in
Southeast Asian frogs. Syst. Biol.
45,278
-289.
Engler, G., Fogarty, C. M., Banks, J. R. and Zupanc, G. K. (2000). Spontaneous modulations of the electric organ discharge in the weakly electric fish, Apteronotus leptorhynchus: a biophysical and behavioral analysis. J. Comp. Physiol. A 186,645 -660.[CrossRef][Medline]
Felsenstein, J. (1985). Phylogenies and the comparative method. Am. Nat. 125, 1-15.[CrossRef]
Garland, T. and Adolph, S. C. (1994). Why not to do 2-species comparative studies – limitations on inferring adaptation. Physiol. Zool. 67,797 -828.
Hagedorn, M. and Heiligenberg, W. (1985). Court and spark – electric signals in the courtship and mating of gymnotoid fish. Anim. Behav. 33,254 -265.[CrossRef]
Heiligenberg, W. and Bastian, J. (1980). Species specificity of electric organ discharges in sympatric gymnotoid fish of the Rio Negro. Acta Biol. Venez. 10,187 -203.
Heiligenberg, W., Metzner, W., Wong, C. J. and Keller, C. H. (1996). Motor control of the jamming avoidance response of Apteronotus leptorhynchus: evolutionary changes of a behavior and its neuronal substrates. J. Comp. Physiol. A 179,653 -674.[CrossRef][Medline]
Hopkins, C. D. (1974a). Electric communication in the reproductive behavior of Sternopygus macrurus (Gymnotoidei). Z. Tierpsychol. 35,518 -535.[Medline]
Hopkins, C. D. (1974b). Electric communication: functions in the social behavior of Eigenmannia virescens.Behaviour 50,270 -305.[CrossRef]
Hopkins, C. D. and Heiligenberg, W. (1978). Evolutionary designs for electric signals and electroreceptors in gymnotid fishes of Surinam. Behav. Ecol. Sociobiol. 3, 113-134.[CrossRef]
Kawasaki, M., Maler, L., Rose, G. J. and Heiligenberg, W. (1988). Anatomical and functional-organization of the prepacemaker nucleus in gymnotiform electric fish – the accommodation of two behaviors in one nucleus. J. Comp. Neurol. 276,113 -131.[CrossRef][Medline]
Kolodziejski, J. A., Nelson, B. S. and Smith, G. T. (2005). Sex and species differences in neuromodulatory input to a premotor nucleus: a comparative study of substance P and communication behavior in weakly electric fish. J. Neurobiol. 62,299 -315.[CrossRef][Medline]
Kolodziejski, J. A., Sanford, S. E. and Smith, G. T.
(2007). Stimulus frequency differentially affects chirping in two
species of weakly electric fish: implications for the evolution of signal
structure and function. J. Exp. Biol.
210,2501
-2509.
Kramer, B., Kirschbaum, F. and Markl, H. (1981). Species specificity of electric organ discharges in a sympatric group of Gymnotoid fish from Manaus (Amazonas). In Sensory Physiology of Aquatic Lower Vertebrates. Vol.31 (ed. T. Szabó and G. Czéh), pp.195 -219. Budapest: Pergamon Press/Akadémiai Kiadó.
Larimer, J. L. and MacDonald, J. A. (1968).
Sensory feedback from electroreceptors to electromotor pacemaker centers in
gymnotids. Am. J. Physiol.
214,1253
-1261.
Mago-Leccia, F., Lundberg, J. G. and Baskin, J. N. (1985). Systematics of the South American freshwater fish genus Adontosternarchus (Gymnotiformes, Apteronotidae). Contrib. Sci. Nat. Hist. Mus. Los Angeles Co. 358, 1-19.
Martins, E. P. (1996). Conducting phylogenetic comparative studies when the phylogeny is not known. Evolution 50,12 -22.[CrossRef]
Martins, E. P. (1999). Estimation of ancestral
states of continuous characters: A computer simulation study. Syst.
Biol. 48,642
-650.
Martins, E. P. (2004). COMPARE version 4.6. http://compare.bio.indiana.edu/.
Martins, E. P. and Hansen, T. F. (1997). Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149,646 -667.[CrossRef]
Martins, E. P. and Lamont, J. (1998). Estimating ancestral states of a communicative display: a comparative study of Cyclura rock iguanas. Anim. Behav. 55,1685 -1706.[CrossRef][Medline]
Martins, E. P., Diniz, J. A. F. and Housworth, E. A. (2002). Adaptive constraints and the phylogenetic comparative method: a computer simulation test. Evolution 56, 1-13.[CrossRef][Medline]
Martins, E. P., Labra, A., Halloy, M. and Thompson, J. T. (2004). Large-scale patterns of signal evolution: an interspecific study of Liolaemus lizard headbob displays. Anim. Behav. 68,453 -463.[CrossRef]
Metzner, W. (1999). Neural circuitry for communication and jamming avoidance in gymnotiform electric fish. J. Exp. Biol. 202,1365 -1375.[Abstract]
Meyer, J. H. (1984). Steroid influences upon discharge frequencies of intact and isolated pacemakers of weakly electric fish. J. Comp. Physiol. A 154,659 -668.[CrossRef]
Moortgat, K. T., Keller, C. H., Bullock, T. H. and Sejnowski, T.
J. (1998). Submicrosecond pacemaker precision is behaviorally
modulated: the gymnotiform electromotor pathway. Proc. Natl. Acad.
Sci. USA 95,4684
-4689.
Nishikawa, K. C. (1997). Emergence of novel functions during brain evolution. Bioscience 47,341 -354.[CrossRef]
Ord, T. J. and Martins, E. P. (2006). Tracing the origins of signal diversity in anole lizards: phylogenetic approaches to inferring the evolution of complex behaviour. Anim. Behav. 71,1411 -1429.[CrossRef]
Ord, T. J. and Stuart-Fox, D. (2006). Ornament evolution in dragon lizards: multiple gains and widespread losses reveal a complex history of evolutionary change. J. Evol. Biol. 19,797 -808.[CrossRef][Medline]
Price, J. J. and Lanyon, S. M. (2002). Reconstructing the evolution of complex bird song in the oropendolas. Evolution 56,1514 -1529.[CrossRef][Medline]
Rasnow, B. and Bower, J. M. (1996). The electric organ discharges of the gymnotiform fishes. 1. Apteronotus leptorhynchus. J. Comp. Physiol. A 178,383 -396.
Rasnow, B., Assad, C. and Bower, J. M. (1993). Phase and amplitude maps of the electric organ discharge of the weakly electric fish, Apteronotus leptorhynchus. J. Comp. Physiol. A 172,481 -491.[CrossRef][Medline]
Ryan, M. J. (2005). The evolution of behaviour, and integrating it towards a complete and correct understanding of behavioural biology. Anim. Biol. 55,419 -439.[CrossRef]
Schaefer, J. E. and Zakon, H. H. (1996).
Opposing actions of androgen and estrogen on in vitro firing
frequency of neuronal oscillators in the electromotor system. J.
Neurosci. 16,2860
-2868.
Serrano-Fernandez, P. (2003). Gradual frequency rises in interacting black ghost knifefish, Apteronotus albifrons.J. Comp. Physiol. A 189,685 -692.[CrossRef][Medline]
Smith, G. T. (1999). Ionic currents that contribute to a sexually dimorphic communication signal in weakly electric fish. J. Comp. Physiol. A 185,379 -387.[CrossRef][Medline]
Smith, G. T. (2006). Pharmacological characterization of ionic currents that regulate high-frequency spontaneous activity of electromotor neurons in the weakly electric fish, Apteronotus leptorhynchus. J. Neurobiol. 66, 1-18.[CrossRef][Medline]
Smith, G. T. and Zakon, H. H. (2000). Pharmacological characterization of ionic currents that regulate the pacemaker rhythm in a weakly electric fish. J. Neurobiol. 42,270 -286.[CrossRef][Medline]
Smith, G. T., Lu, Y. and Zakon, H. H. (2000). Parvocells: a novel interneuron type in the pacemaker nucleus of a weakly electric fish. J. Comp. Neurol. 423,427 -439.[CrossRef][Medline]
Spicer, J. (2004). Making Sense of Multivariate Data Analysis. Thousand Oaks, CA: Sage.
Steinbach, A. B. (1970). Diurnal movements and
discharge characteristics of electric gymnotid fishes in Rio-Negro, Brazil.
Biol. Bull. 138,200
-210.
Stoddard, P. K. (1999). Predation enhances complexity in the evolution of electric fish signals. Nature 400,254 -256.[CrossRef][Medline]
Tallarovic, S. K. and Zakon, H. H. (2002). Electrocommunication signals in female brown ghost electric knifefish, Apteronotus leptorhynchus. J. Comp. Physiol. A 188,649 -657.[CrossRef][Medline]
Triefenbach, F. and Zakon, H. (2003). Effects of sex, sensitivity and status on cue recognition in the weakly electric fish Apteronotus leptorhynchus. Anim. Behav. 65, 19-28.[CrossRef]
Zakon, H., Oestreich, J., Tallarovic, S. and Triefenbach, F. (2002). EOD modulations of brown ghost electric fish: JARs, chirps, rises, and dips. J. Physiol. Paris 96,451 -458.[CrossRef][Medline]
Zhou, M. and Smith, G. T. (2006). Structure and
sexual dimorphism of the electrocommunication signals of the weakly electric
fish, Adontosternarchus devenanzii. J. Exp. Biol.
209,4809
-4818.
Zupanc, G. K. H. and Maler, L. (1993). Evoked chirping in the weakly electric fish Apteronotus leptorhynchus – a quantitative biophysical analysis. Can. J. Zool. 71,2301 -2310.[CrossRef]
Zupanc, G. K. H. and Maler, L. (1997). Neuronal control of behavioral plasticity: the prepacemaker nucleus of weakly electric gymnotiform fish. J. Comp. Physiol. A 180,99 -111.[CrossRef]
Zupanc, G. K. H., Sîrbulescu, R. F., Nichols, A. and Ilies, I. (2006). Electric interactions through chirping behavior in the weakly electric fish, Apteronotus leptorhynchus. J. Comp. Physiol. A 192,159 -173.[CrossRef][Medline]
Zupanc, M. M., Engler, G., Midson, A., Oxberry, H., Hurst, L. A., Symon, M. R. and Zupanc, G. K. H. (2001). Light-dark-controlled changes in modulations of the electric organ discharge in the teleost Apteronotus leptorhynchus. Anim. Behav. 62,1119 -1128.[CrossRef]
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