Variation in morphology and performance of predator-sensing system in wild cricket populations
Université de Tours, IRBI UMR CNRS 6035, Parc Grandmont, 37200 Tours, France
* Author for correspondence (e-mail: olivier.dangles{at}univ-tours.fr)
Accepted 1 November 2004
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Summary |
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Key words: air current detection, biomechanical model, mechanoreceptors, predator-prey interactions, sensory ecology
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Introduction |
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Given the centrality of predation in the spectrum of ecological
interactions, focusing on predator detection by animals offers an ideal
opportunity for testing the functional relationships between morphology and
performance. Adaptations of prey to deal with environmental variables and
risks of predation are likely to occur at the detection stage as it confers
substantial selective advantages on prey
(Endler, 1991;
Fox et al., 2001
). Many
arthropod species have evolved high performance detection systems consisting
of mechanoreceptive cuticular hairs sensitive to the slightest air
displacement, such as that generated by approaching predators. Patterns of air
movement are, therefore, an important source of information for crickets
(Tobias and Murphey, 1979
),
cockroaches (Camhi et al.,
1978
), caterpillars (Tautz and
Markl, 1978
) and crayfish
(Breithaupt et al., 1995
).
Among them, crickets have the best sensors
(Barth, 2002
) as they can
detect air signals of <0.03 mm s-1
(Shimozawa et al., 2003
).
These hair receptors act as efficient predator sensors.
Cricket cerci are covered with hundreds of filiform hairs that excite giant
interneurons and induce escape behaviour from predators
(Edwards and Palka, 1974;
Miller et al., 1991
). A
variation of hair length fractionates both the intensity and frequency range
of an air stimulus (Shimozawa and Kanou,
1984
) and, as a result, the morphology of the cercal sensory
system is tightly linked to cricket perception. Based on a
fluidmechanical theory of air movement around hairs, an extensive
development of cricket perception modelling has been performed in the last
decades (Tautz, 1979
;
Shimozawa and Kanou, 1984
;
Humphrey et al., 1993
;
Shimozawa et al., 1998
;
Humprey et al., 2003
;
Shimozawa et al., 2003
).
Recently, Magal et al. (C. Magal, O. Dangles, P. Caparroy and J. Casas,
manuscript submitted for publication) have built a model that links the
morphology and biomechanics of the entire cercal hair canopy and the response
of crickets to approaching predators.
We investigated how morphological variability of cricket sensors translates
into functional variability of predatorprey interactions. Although
evolutionary biologists have long recognized plasticity of traits, sensory
ecology theory has rarely incorporated trait variability into predictions of
the response of organism to predators (see
Barth and Schmid, 2001).
Crickets experience a wide range of environments and predatory communities
where they need to maximize the performance of their sensory system. We thus
explored the natural range of variability for air sensors in wild populations
of wood crickets (Nemobius sylvestris) sampled in a variety of
habitats. We further assessed how these levels of variability influence air
movement perception in crickets using a biomechanical model of cercal hair
population coding.
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Material and methods |
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Cricket sampling
In August 2002, we carried out cricket sampling from five geographically
widespread locations in France. Study sites were selected to include
latitudinal (from 49°01'50''N to 43°36'43''N),
longitudinal (from 06°05'52''E to 01°12'30''W)
and climatic gradients (Table
1). We maximised site heterogeneity also at the habitat scale by
including different types of environments that wood crickets live in: oak
forest, pine forest, grassland, open woodland and Mediterranean scrubland. At
each site we surveyed predator communities by pitfall trapping (6 cm diameter,
24 h), quadrate sampling (50 x50 cm, three replicates), and net
collecting (30 min). Wandering and flying predators were both present at all
sites but the dominant taxa varied among sites
(Table 1). At each site, ten
adult female crickets were hand-netted and stored in 70° ethanol. We
studied the adult female phenotypes as they are easily identifiable in the
field by their ovipositor, which are longer than the cerci. Only crickets with
fully intact and normal cerci were used for measurements.
|
Cercal traits measurements
Adult wood crickets typically bear about 350 cercal filiform hairs divided
into two cohorts (Edwards and Palka,
1974; C. Magal, O. Dangles, P. Caparroy and J. Casas, manuscript
submitted for publication): short hairs (<500 µm) with a median length
around 150 µm and long hairs (>500 µm) with a median length around
750 µm. It was impossible to measure all hairs for the 50 studied
individuals. Exhaustive hair surveys over the cerci require acquisition and
analysis of 40 SEM pictures per cerci, which needs around 60 man-hours of work
per cerci. We therefore focused on long hairs because they were easily
measured using a stereomicroscope but also because they are more sensitive
than short hairs to low-velocity air currents and assure early detection of
predators (Shimozawa and Kanou,
1984
; Gnatzy and Kämper,
1990
; C. Magal, O. Dangles, P. Caparroy and J. Casas, manuscript
submitted for publication).
In the laboratory, the animals' right cercus was removed and mounted on a broken capillary electrode, which was mounted on a positioning stage allowing three axes of translation and one axis of rotation. The length of long hairs was measured with a dissecting microscope (Leica, MZ 12.5; Bannockburn, IL, USA) and calibrated ocular micrometer. The positioning stage permitted accurate measurements as each hair could be placed in a plane perpendicular to the microscope. Repeated measurements of identified hairs (N=30) on the same animal revealed that the experimental measurement error associated with the use of the ocular protractor was low (<5%). Other morphological measurements including total body length, cercus length and cercus diameter at the base were performed for each cricket.
Statistical analysis
We performed a canonical discriminant analysis (CDA) to test multivariate
differences among cricket populations and to identify which morphological
variables were most useful for discriminating among populations
(ter Braak, 1988). Discriminant
analysis is related to multivariate analysis of variance and to multiple
regression. It is particularly efficient to test multivariate differences
among groups, but also to explore which variables are most useful for
discriminating among groups. CDA finds linear combinations of discriminating
variables that maximize the differences between groups and minimizes the
differences within the groups. We considered six variables to describe the
wind-sensitive cercal system of the crickets: (1) the total length of the
circus; (2) the length of the cercus relative to body length; (3) the total
number of hairs; (4) the density of hairs (total number of hairs relative to
the surface of the conic-shaped cercus); (5) the median length of the sampled
hairs; and (6) the number of hairs longer than 1000 µm (very long hairs are
likely to be the most efficient hairs for early detection of predators). The
Wilk's lambda test was used to test whether the differences explained by the
discriminant variables were significant. Only statistically significant
discriminating variables were retained in the explanation of the results. We
used the Mahalanobis distance (D2) to discriminate between groups
a large value indicating good discrimination. It was converted to an
F-statistic to test if the populations were significantly different from each
other (Klecka, 1980
).
Modelling the sensitivity response of crickets to air signals
The model we developed aims at reproducing the cercal population coding of
oscillatory air flows by the hundreds of hairs on cerci. Its building blocks
are the biomechanics of hair movement, the distribution of hair length in the
canopy, the relationship between single hair movement and its
neurophysiological activity and the overall canopy response (C. Magal, O.
Dangles, P. Caparroy and J. Casas, manuscript submitted for publication).
Details of parameter values and code implementation can be found in the above
reference.
The mechanical behaviour of hair movement in an oscillating fluid has been
modelled extensively in the past (Tautz,
1979) and our model is identical to those developed so far (see
Humphrey et al., 2003
;
Shimozawa et al., 2003
for
latest reviews). A filiform hair is defined as an inverted pendulum with a
rigid shaft supported by a spring at the base. The system can be described by
four parameters: the moment of inertia that represents the mass distribution
along the hair shaft; the spring stiffness, which provides the restoring
torque towards the resting position; the torsional resistance within the hair
base; and the coupling resistance between hair shaft and the air. For a rigid
hair oscillating relative to a fixed axis of rotation, conservation of angular
momentum L(t) states that the rate of change of angular momentum is
equal to the sum of torques acting on the hair:
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Long hairs have their peak response at low frequency, while shorter hairs
display a flat response over most of the frequency range, with a peak response
at high frequency. As a consequence, hair number and the variation of hair
length are key features of the cercal system of crickets as they fractionate
both the intensity and the frequency range of an air stimulus. The model uses
several approximations and is precisely valid for a hair on a plate, with the
fluid oscillating in the plane of the plate. These approximations have been
tested and found appropriate for a hair on a cercus in a flow oscillating
parallel to it. The maximal angular hair deflection during a single
oscillation is used for modelling electrophysiological activity. We assumed
here that the action potential frequency in the associated cercal afferent was
directly proportional to the maximal angular deflection. Population coding was
done in an additive way, borrowing the approach used for the vector coding in
the cricket sensory system (Jacobs,
1995; Dayan and Abbott,
2001
). The canopy response is therefore the sum, over each hair
length, of the maximal hair deflection multiplied with the number of hairs of
that length. Its units are radians. The cercal best frequency is the frequency
at which the canopy response reaches its maximal value. The cercal best
frequency shifts to lower frequencies and to higher response levels with
increasing air velocity.
In the present study, we implemented the model with the hair length
distributions measured for the five cricket populations. We thereby obtained
the cercal canopy response as a function of signal frequency for each
population for hairs longer than 500 µm. By selecting ecologically relevant
acoustic signals, such as those generated by running (30 Hz) and flying
(
170 Hz) predators (Gnatzy and
Kämper, 1990
), we evaluated the sensitivity response of
crickets to natural predators.
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Results |
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|
|
Hair length distributions
Because hair number and hair size were both significant canonical
variables, we built the hair length distribution diagram for each mean
population (Fig. 2).
Populations had heterogeneous hair length distribution with a number of hairs
ranging from 32 (Pop. 1) to 49 (Pop. 3) and a median hair length from 711
µm (Pop. 1) to 809 µm (Pop. 5). Hairs longer than 1000 µm were
abundant in populations 4 and 5 but rare in population 1
(Fig. 2; grey bars).
|
Modelling of cricket perception
The integration of hair length distributions into the model revealed that
the cercal canopy response to an oscillating signal of various frequencies
varied among populations (Fig.
3A). The response was higher for populations having more hairs
(Pop. 3 and Pop. 4) than populations with fewer hairs (Pop. 1 and Pop. 5).
However, the best detected frequency (maximum of the curve) was rather similar
among populations, between 57.5 and 62.5 Hz. To better visualize
among-population difference in the canopy response, we calculated the percent
of variation in the canopy response of the five populations from a `reference'
population (determined as the mean of canopy responses of all populations,
Fig. 3B). On average, the more
sensitive populations (Pop. 3 and Pop. 4) had a canopy response 35%
higher than less sensitive ones (Pop. 1 and Pop. 5;
Fig. 3B). These differences
varied across signal frequencies: the decreasing pattern of Pop. 4 and Pop. 5
suggests that populations with the largest proportion of long hairs had an
increased sensitivity at low frequencies; the opposite pattern was observed
for other populations (Fig.
3B).
|
The measured hair length distribution heterogeneity had strong functional
implications for the perception of natural predators among cricket populations
(Fig. 4A,B). Whatever the type
of predator signal considered (running, 30 Hz, or flying, 170 Hz; see
Fig. 3A), cricket perception
level increased with predator signal intensity. For any perception level, the
more sensitive populations (Pop. 3 and Pop. 4) were able to detect air signal
intensity 40% lower than that detected by the less sensitive population
(Pop. 1). This pattern was consistent for both types of predator signals,
although Pop. 5 had a better average perception level at low rather than high
frequencies. In this case, the presence of very long hairs (>1100 µm)
partly compensated for the low number of hairs.
|
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Discussion |
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Morphological variation has been quantified in both invertebrates and
vertebrates (Archer et al.,
1987; Ayala et al.,
1993
; Shyue et al.,
1995
; Jokela et al.,
2003
; Spaethe and Chittka,
2003
; Opstad et al.,
2004
) but most studies have been restricted to visual systems.
This study confirms that morphological variation is also found in the air-flow
sensory system of wild crickets. Variation is expressed both in terms of hair
densities and hair length frequencies on the cerci, two characteristics that
greatly influence cricket perception
(Shimozawa and Kanou,
1984
).
Sensor performance
We do not know to what extent the measured variation characterizes the full
range of variability expressed in natural populations but this variation has
strong functional implications for the detection of predator signals. To our
knowledge, none of the numerous models built to understand animal perception
(Neumann, 2002;
Ritz et al., 2000
;
Svensen and Kiorboel, 2000
;
Erwin et al., 2001
), has ever
incorporated the potential structural variability of a sensor to predict
consequences on the performance of animals. Notable exceptions are the recent
studies performed by Spaethe et al.
(2001
) and Spaethe and Chittka
(2003
) who suggest that
inter-individual variation in the morphology of the compound eye and the
performance of the linked neural circuitry influences foraging efficiency of
bumblebees under natural conditions. They found that the larger ommatidial
diameter in larger bumble bees could make a sevenfold increase in sensitivity.
Here the threshold sensitivity of the more sensitive cricket population was
35% lower than the less sensitive one and was independent on the size of the
individuals. Although we disregarded short hairs (<500 µm) in this
study, their contribution is not likely to change the measured variability
because low-velocity air signals, such as those emitted by natural predators
in the early approaching phase, are mainly perceived by hairs longer than 500
µm.
Ecology, evolution and fitness of crickets
Both morphological and performance variation in cricket sensors exist at
individual and population levels. However, the latter largely overcame the
former suggesting that our results can be analyzed in ecological and
evolutionary terms. Cricket sensing variability might be a consequence of
genetic differentiation resulting from selection, or may be due to the effect
of different environments on the expression of phenotype. For example, the
high degree of reproductive isolation among cricket populations
(Fulton, 1952;
Mousseau and Roff, 1989
), the
influence of the habitat structure on air signal transmission (e.g.
Dusenbery, 1992
;
Bradbury and Vehrencamp, 1998
)
or the inducible response of insects to predatory pressure (e.g.
Weisser et al., 1999
) are
potential hypotheses to explain measured morphological and performance
variability.
To investigate the relationship between performance (e.g. escape success)
and ecology (sensu Arnold,
1983), the morphological variation has to be in turn translated
into fitness variation associated with predator escape. However, while
crickets and cockroaches are reported to encode information on their fluid
dynamical environment (Rinberg and
Davidowitz, 2000
), the information available for crickets during
predatorprey interaction in the field remains unknown as only a couple
of laboratory measurements of the predator signal have been conducted
(Tautz and Markl, 1978
;
Gnatzy, 1996
). Some of these
experiments can, however, provide helpful data to discuss the fitness
relevance of cricket sensor variability. For example, the negative
relationship (y=1/x2) between air amplitude
displacement of the predator signal (y) and the distance to the
predator (x) quantified by Tautz and Markl
(1978
) allowed us to calculate
the signal intensity of predator (equal to 2
x signal frequency
x y). We thus estimate that crickets belonging to the more
sensitive population (Pop. 3, at predator signal intensity=0.31 mm
s-1; see Fig. 4B)
could perceive an approaching flying predator at a distance (x) 20%
higher than the less sensitive ones (at predator signal intensity=0.47). This
is valid up to a maximal distance of 70 cm
(Tautz and Markl, 1978
).
Because long hairs are extremely useful to detect predators in the far field
(low signal intensity), having more and longer hairs could confer substantial
selective advantage on crickets.
In conclusion, the present study is a first bridge between the numerous
classical neuroethological studies and the ecological and evolutionary
understanding of the exceedingly well performing cricket air-flow sensor. We
quantified for the first time the natural variability in sensor morphology of
an insect prey which may translate into variability of predator detection.
This study needs further work to interpret the potential adaptive significance
of cercal trait patterns among cricket populations. First, studies on a larger
set of populations should allow us to disentangle genetically and
environmentally induced sources of variation. Second, we need to confirm that
the variation in the phenotype increases fitness in the environment
encountered. As proposed by Irschick
(2003), measuring performance
of animals in the field is a potential avenue for linking morphology and
fitness of these organisms.
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Acknowledgments |
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References |
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![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Archer, S. N., Endler, J. A., Lythgoe, J. N. and Partridge, J. C. (1987). Visual pigment polymorphism in the guppy Poecilia reticulata. Vision Res. 27,1243 -1252.[CrossRef][Medline]
Arnold, S. J. (1983). Morphology, performance and fitness. Am. Zool. 23,347 -361.
Ayala, F. J., Chang, B. S. W. and Hartl, D. L. (1993). Molecular evolution of the Rh3 gene in Drosophila.Genetica 92,23 -32.[CrossRef][Medline]
Barth, F. G. (2002). A Spider's World: Senses and Behavior. Berlin: Springer-Verlag.
Barth, F. G. and Schmid, A. (2001). Ecology of Sensing. Berlin: Springer-Verlag.
Bellmann, H. and Luquet, G. (1995). Guide des Sauterelles, Grillons et Criquets d'Europe Occidentale. Delachaux et Niestlé.
Bradbury, J. W. and Vehrencamp, S. L. (1998). Principles of Animal Communication. Sinauer Associates.
ter Braak, C. J. F. (1988). Partial canonical correspondence analysis. In Classification and Related Methods of Data Analysis (ed. H. H. Block), pp.551 -558. Amsterdam: North Holland Press.
Breithaupt, T., Schmitz, B. and Tautz, J. (1995). Hydrodynamic orientation of crayfish (Procambarius-Clarkii) to swimming fish prey. J. Comp. Physiol. A 177,481 -491.[Medline]
Bronmark, C. and Miner, J. (1992). Predator-induced phenotypical change in body morphology in Crucian Carp. Science 258,1348 -1350.
Cade, W. H. (1984). Effects of fly parasitoids on nightly calling duration in field crickets (Gryllus integer). Can. J. Zool. 62,226 -228.
Camhi, J. M., Tom, W. and Volman, S. (1978). The escape behavior of the cockroach Periplaneta Americana II. Detection of natural predators by air displacement. J. Comp. Physiol. A 128,203 -212.
Campan, R. (1965). Etude du cycle biologique du grillon Nemobius sylvestris dans la région toulousaine. Bull. Soc. Hist. Nat. Toulouse 100,371 -378.
Chittka, L. and Briscoe, A. (2001). Why sensory ecology needs to become more evolutionary Insect color vision as a case in point. In Ecology of Sensing (ed. F. G. Barth and A. Schmid), pp. 19-37. Berlin: Springer Verlag.
Dayan, P. and Abbott, L. F. (2001). Theoretical Neuroscience: Computational and Mathematical Modelling of Neural Systems. Cambridge: The MIT Press.
Dusenbery, D. B. (1992). Sensory Ecology, How Organisms Acquire and Respond to Information. New York: W. H. Freeman and Co.
Edgar, W. D. (1969). Prey and predators of the wolf spiders Lycosa lugubris. J. Zool. 159,405 -411.
Edwards, J. S. and Palka, J. (1974). The cerci and abdominal giant fibres of the house cricket Acheta domesticus. I Anatomy and physiology of normal adults. Proc. R. Soc. Lond. B. Biol. Sci. 185,83 -103.[Medline]
Endler, J. A. (1991). Interactions between predators and prey. In Behavioural Ecology: An Evolutionary Approach (ed. J. R. Krebs and N. B. Davies), pp.169 -196. Oxford: Blackwell Scientific.
Erwin, H., Wilson, W. W. and Moss, C. F. (2001). A computational model of sensorimotor integration in bat echolocation. J. Acoust. Soc. Am. 110,1176 -1187.[CrossRef][Medline]
Fabre, J. H. (1925). Les souvenirs entomologiques. Tome II. Plon.
Fox, C. W., Roff, D. A., Fairbain, D. J. (2001). Evolutionary Ecology, Concepts and Case Studies. Oxford: Oxford University Press.
Fulton, B. B. (1952). Speciation in the field cricket. Evolution 6,283 -295.
Gabbutt, P. D. (1959). The bionomics of the wood cricket, Nemobius sylvestris (Orthoptera: Gryllidae). J. An. Ecol. 28,15 -42.
Gnatzy, W. (1996). Digger wasp vs. cricket: neuroethology of a predator-prey interaction. Information Processing in Animals 10.
Gnatzy, W. and Kämper, G. (1990). Digger wasp against cricket: II A signal produced by a running predator. J. Comp. Physiol. A 167,551 -556.
Hanisak, M. D., Littler, M. M. and Littler, D. S. (1988). Significance of macroalgal polymorphism: intraspecific tests of the functional-form model. Mar. Biol. 99,157 -166.[CrossRef]
Humphrey, J. A. C., Devarakonda, R., Iglesias, I. and Barth, F. G. (1993). Dynamics of arthropod filiform hairs. I. Mathematical modelling of the hair and air motions. Phil. Trans. R. Soc. Lond. B 340,423 -444.
Humphrey, J. A. C., Barth, F. G. and Voss, K. (2003). The motion-sensing hairs of arthropods: Using physics to understand sensory ecology and adaptive evolution. In Ecology of Sensing (ed. F. G. Barth and A. Schmid), pp.105 -115. Berlin: Springer-Verlag.
Irschick, D. J. (2003). Measuring performance in Nature: implications for studies of fitness within populations. Integr. Comp. Biol. 43,396 -407.
Jacobs, G. A. (1995). Detection and analysis of air currents by crickets. BioScience 45,776 -785.
Jokela, M., Vartio, A., Paulin, L., Fyhrquist-Vanii, N. and
Donner, K. (2003). Polymorphism of the rod visual pigment
between allopatric populations of the sand goby (Potamoschistus
minutus): a microspectrophotometric study. J. Exp.
Biol. 206,2611
-2617.
Klecka, W. R. (1980). Discriminant Analysis. Beverly Hills: Sage Publications.
Kölliker-Ott, U. M., Blows, M. W. and Hoffmann, A. A. (2003). Are wing size, wing shape and asymmetry related to field fitness of Trichogramma egg parasitoids? Oikos 100,563 -573.[CrossRef]
Miller, J. P., Jacobs, G. A. and Theunissen, F. E.
(1991). Representation of sensory information in the cricket
cercal sensory system I. Response properties of the primary interneurons.
J. Neurophysiol. 66,1680
-1989.
Mousseau, T. A. and Roff, D. A. (1989). Geographic variability in the incidence and heritability of wing dimorphism in the striped ground cricket, Allonemobius fasciatus.Heredity 62,315 -318.
Neumann, T. R. (2002). Modeling insect compound eyes: Space-variant spherical vision. In Proceedings of the 2nd International Workshop on Biologically Motivated Computer Vision (ed. H. H. Bülthoff, S.-W. Lee, T. Poggio and C. Wallraven,), pp.360 -367. Berlin: Springer-Verlag.
Norberg, U. M. (1994). Wing design, flight performance and habitat use in bats In Ecological morphology. In Integrative Organismal Biology (ed. P. C. Wainwright and S. M. Reilly), pp. 205-239. Chicago: Chicago University Press.
Opstad, R., Rogers, S. M., Behmer, S. T. and Simpson, S. J. (2004). Behavioural correlates of phenotypic plasticity in mouthpart chemoreceptor numbers in locust. J. Ins. Physiol. 50,725 -736.[CrossRef][Medline]
Ponsard, S. and Arditi, R. (2000). What can stable isotopes (15N and 13C) tell about the food web of soil invertebrates? Ecology 81,852 -864.
Rinberg, D. and Davidowitz, H. (2000). Insect perception: Do cockroaches `know' about fluid dynamics? Nature 405,756 .[Medline]
Ritz, T. Adem, S. and Schulten, K. (2000). A
model for photoreceptor-based magnetoreception in birds. Biophys.
J. 78,707
-718.
Schlichting, C. D. and Pigliucci, M. (1998). Phenotypic Evolution: A Reaction Norm Perspective. Sunderland, MA: Sinauer Associates.
Shimozawa, T. and Kanou, M. (1984). Varieties of filiform hairs: range fractionation by sensory afferents and cercal interneurons of a cricket. J. Comp. Physiol. A 155,485 -493.
Shimozawa, T., Kumagai, T. and Baba, Y. (1998). Structural scaling and functional design of the cercal wind-receptor hairs of cricket. J. Comp. Physiol. A 183,171 -186.
Shimozawa, T., Murakami, J. and Kumagai, T. (2003). Cricket wind receptors: Thermal noise for the highest sensitivity known. In Sensors and Sensing in Biology and Engineering. (ed. F. G. Barth, J. A. C. Humphrey and T. Secomb), pp. 145-157. Berlin: Springer-Verlag.
Shyue, S. K., Hewettemmett, D., Sperling, H. G., Hunt, D. M., Bowmaker, J. K., Mollon, J. D. and Li., W. H. (1995). Adaptive evolution of color-vision genes in higher primates. Science 269,1265 -1267.[Medline]
Spaethe, J., Tautz, J. and Chittka, L. (2001).
Visual constraints in foraging bumblebees: Flower size and color affect search
time and flight behavior. Proc. Natl. Acad. Sci. USA
98,3898
-3903.
Spaethe, J. and Chittka, L. (2003).
Interindividual variation of eye optics and single object resolution in
bumblebees. J. Exp. Biol.
206,3447
-3453.
Svanbäck, R. and Eklöv, P. (2003). Morphology dependent foraging efficiency in perch: a trade-off for ecological specialization? Oikos 102,273 -284.[CrossRef]
Svensen, C. and Kiorboel, T. (2000). Remote
prey detection in Oithona similis: hydromechanical versus chemical
cues. J. Plankton Res.
22,1155
-1166.
Tautz, J. (1979). Reception of particle oscillation in a medium An unorthodox sensory capacity. Naturwiss. 66,452 -461.[CrossRef]
Tautz, J. and Markl, H. (1978). Caterpillars detect flying wasps by hairs sensitive to airborne vibrations. Behav. Ecol. Sociobiol. 4, 101-110.[CrossRef]
Tobias, M. and Murphey, R. K. (1979). The response of cercal receptors and identified interneurons in the cricket (Acheta domesticus) to airstreams. J. Comp. Physiol. A 129,51 -59.
Van Damme, R., Aerts, P. and Vanhooydonck, B. (1998). Variation in morphology, gait characteristics and speed of locomotion in two populations of lizards. Biol. J. Lin. Soc. 63,409 -427.[CrossRef]
Wainwright, P. C. and Reilly, S. M. (1994). Ecological Morphology Integrative Organismal Biology. Chicago: Chicago University Press.
Weisser, W. W., Braendle, C. and Minoretti, N. (1999). Predator-induced morphological shift in the pea aphid (Acyrthosiphon pisum). Proc. R. Soc. Lond. B 266,1175 -1182.[CrossRef]
Werner, E. E. and Peacor, S. D. (2003). A review of trait-mediated indirect interactions in ecological communities. Ecology 84,1083 -1100.
West-Eberard, M. J. (2003). Developmental Plasticity And Evolution. Oxford: Oxford University Press.
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