Abstract Pachygrapsis crassipes, the lined purple shore crab, is equipped with extremely sensitive sensory hairs on its walking legs. These hairs potentially play a major role in mediating the crab’s sensory relationship with its environment, including encoding vital information such as the approach of predators. This study examined the vibratory sensitivity of these tactile sensors in order to quantify the frequency range to which they respond, as well as their absolute level of sensitivity. Electrophysiological methods were used to determine the sensors' threshold to impulse-like stimuli. Neuronal responses were then tested over a wide range of frequencies and intensities to more completely quantify the sensitivity range. Introduction In the middle of the 20" century, pioneer German ethologist Baron Jacob Von Uexkull introduced the idea that all animals live within unique, species-specific perceptual worlds which he entitled their “Umwelt". A species' Umwelt defines its environment in relation to its sensory capabilities. Information outside of these capabilities is imperceptible and, therefore, effectively does not exist. In the 1934 essay entitled "A Stroll through the Worlds of Animals and Men: A picture book of invisible worlds", Uexkull described the Umwelt as being “like soap-bubble surrounding an individual being, filtering all it sees and feels." In less ethereal terms, the Umwelt represents the result of a long-period of evolutionary adaptation to a particular environment, which has led to the ability to detect and filter sensory cues essential to survival, and ignore uninformative sensory "noise". To this effect, the Umwelt's of different species have different richness, often corresponding to the complexity of their behavior. A classic example of an “impoverished" Umwelt is that of a tick, which is sensitive to only three specific sensory cues allowing it to detect its mammalian host. This can be strikingly contrasted with the Umwelt of a human, which includes sensitivity to broad color spectra and sound frequency, and highly- developed olfactory and tactile capabilities. This contrast accentuates one of the greatest benefits attained from Uexkull's idea: freedom from the belief that all animals perceive the world in the same manner as humans. This means to truly understand an animal, humans must first understand its sensory capabilities, and the sensory cues which shape and define its Umwelt. Pursuit of this understanding is the goal of the newly emerging scientific field, Sensory Ecology, the integration of sensory physiology and ecology. In a world filled with an incredible amount of information, sensory ecologists seek to quantify what information is filtered out by an animal, and in what way, in order to mediate its interaction with its environment. What information has the animal evolved to define the boundaries of its Umwelt, and how does this information help it be maximally fit? The recognition of restricted windows of sensory sensitivity in animals has drawn attention to a potential new source of human-induced environmental harm. Human activities often introduce a large amount of sensory "noise" into the surrounding environment. One of the most widely recognized types of this sensory pollution is auditory or vibratory noise, which is commonly associated with activities such as construction and transportation. Sound sensitivity varies widely among animals (Fig. 1) and is used in many different ways, often playing a vital role in communication and predator-prey interactions. Human-produced sound that falls withir the frequency range of these environmental cues could confuse or limit an animal’s ability to perceive important sound signals, and therefore reduce its fitness. Studies examining this possibility have recently been undertaken by the navy and the Air Force, both of which create a substantial amount of vibrational noise in the environment. The navy, which often uses high amplitude acoustic signals under water, examined the frequency sensitivity range of cetaceans, such as dolphins and whales, which rely on acoustic signals for communication. In one such study they determined that the baleen whale vocalized from 15- 8000 Hz, well within the range of anthropogenic sources. This implied that naval activities could interfere in the whales' communicational channels. In similar examinations, the airforce found that aircraft noise and sonic booms could cause the masking of signals in some species and populations of wildlife (Manci et al. 1988). In addition, airforce researchers found marked secondary effects of environmental noise introduction on various animals, including non¬ auditory impacts such as stress, behavioral changes, mating interference and detrimental changes in the ability to obtain food. In one such study, lab rodents and rabbits continuously exposed to general noise from 50 Hz - 40kHz demonstrated a range of responses, from “anxiety-like" behavior, to complex physiological effects such as increased urinary excretion of sodium and potassium, excretion of vasopressin, and suppressed thyroid activity (Manci et al. 1988). These studies make it clear that quantification of animal sensory sensitivity provides a valid new framework within which to view human environmental impact. It is with this framework in mind that this experiment was designed. The lined purple shore crab, Pachygrapsis crassipes is a common intertidal organism along the central California coast. Sensory hairs on its walking legs are known to function as vibrational sensors which mediate its responsiveness to substrate-borne vibrations (Fig. 2). Numerous studies have been performed on the vibrational sensitivity of the distantly related Uca genus, fiddler crabs, which produce vibrational signals as a part of their mating ritual by rhythmically drumming on rocky substrate with their enlarged chelae (Salmon 1971). These studies thoroughly quantify the vibrational sensitivity and response patterns of these crabs' leg sensors, as well as those of their vibration responsive CNS interneurons (Hall 1985, Aicher and Tautz 1983, Aicher and Tautz 1990, Salmon 1971). Similar studies have been performed to quantify the vibrational sensitivity of cercal hairs in the cricket and cockroach (Buno et al. 1981, Dagan and Camhi 1979, Matsumoto and Murphey 1977, Buno et al. 1981) All of this work demonstrates that vibrational sensitivity is an extremely important element of the arthropod sensory system, playing a diverse and substantial role in their interactions with the environment. Although vibrational sensitivity makes up an important part of the sensory world, or Umwelt, of Pachygrapsis, little is known about the range of vibrational stimuli to which they respond. The goal of this study, therefore, was to begin to quantify this range in order to understand what these crabs "hear" in their noisy vibrational environment. Defining this window of sensitivity is the first step in understanding the possible effects of anthropogenic vibrations introduced into the crab’s environment by human near-shore activity. If these vibrations lie within the sensitivity range of Pachygrapsis, they could be "jamming" the sensitivity channel of the crab and therefore substantially reducing its fitness, and ability to relate to its environment. Materials and methods Subjects: Pachygrapsis crassipes of indiscriminant size, gender and age were collected by hand from the intertidal area of Hopkins Marine Life Refuge. Animals were kept up to three days in a tank which was continuously refreshed by unfiltered seawater. Leg preparation: All experiments were performed on isolated walking legs of the experimental subjects. The legs were pinned in a small dish of seawater and the leg nerve exposed. Extracellular recordings from the nerve were taken with a hook electrode connected to a recording amplifier (Grass Instruments). The amplifier fed to an oscilliscope used to visualize nervous output, as well as a computer data acquisition system using the software program P-Clamp 8. Scanning Electron Microscope: Samples were air dried, mounted on carbon tape and sputter coated with gold, then viewed with a Hitachi S-450 scanning electron microscope at 15 kv. Threshold determination: High-frequency, impulse-like stimuli were produced by dropping differently sized steel ball bearings from various heights onto a steel table upon which the leg was mounted. The ball was dropped from an angled slide attached to a metal stand to ensure that it fell from the desired height. A strain gauge fastened to the table under the slide recorded the exact moment of stimulus application. A custom-built amplifier fed the signal from the gauge into the computer. This data was aligned with the corresponding output from the leg to allow direct comparison between stimulus application and nervous response. Stimulus intensity was measured in Newton-meters (N-m), the gravitational potential energy imparted to the table by ball impact. Varied frequency. To test receptor response to a variety of different frequencies, a speaker cone was fastened to the side of a plastic stand supporting the prepared leg. A function generator fed sinusoidal frequency functions of 10, 50, 100, 200, 500 and 1000 Hz to a stereo amplifier which drove the speaker cone via a power amplifier. Frequencies were applied in random order at two different amplitudes, and approximately every third recording was made without applied stimulus to control for receptor fatigue. Each trial lasted 500 ms and was recorded as a single file by the computer to allow later comparison. Results Threshold determination: Leg response to stimulus application was scored according to a binary scheme. A score of 1 indicated a positive receptor response to applied stimulus, and 0 indicated none (Fig 3). Threshold sensitivity of the leg was stochastic, lying within the range of 1.4 * 10°-3.9 * 10° N- m. Within this range response to stimulus application was variable. Above the range responses were consistently 1 and below they were consistently 0 (Fig 4). Varied frequency. Lower frequency stimuli (10-50 Hz) elicited a phase-locked response in the leg's output train, demonstrated by autocorrelation analysis. Significant correlation in the output signal occurred at intervals corresponding to single cycles of the sinusoidal stimuli. This indicated that the leg was firing a patterned response locked to the frequency of the stimulus (Fig. 5). At frequencies above 50 Hz phase-locking was no longer observed (Fig. 6). Stimulus frequencies from 50-200 Hz elicited an increase in temporal firing rate in the leg nerve. This was measured by counting the number of action potentials surpassing a certain threshold level set by eye directly above background recording noise. Data were normalized to account for small changes in firing rate as the leg fatigued. Normalization was performed by dividing the counts during the test run by the average of the counts from control recordings taken directly before and after. A normalized value greater than 1 indicates an increased firing in response to applied stimuli. All normalized values resulting from the application of a particular stimulus frequency and amplitude were averaged to quantify the general response to stimulus of that intensity. The higher amplitude stimuli consistently elicited a greater temporal firing rate than the lower, although the difference is nearly indistinguishable at a frequency of 1000Hz (Fig. 7). Neither data analysis technique was able to demonstrate sensitivity to vibrational stimuli with frequency greater than 500 Hz. Discussion The threshold determination phase of the experiment proved the crabs' vibrational sensors to be extremely responsive to high-frequency impulse stimuli. Many important environmental signals in the crabs' natural habitat, such as the pecking of predatory birds, produce impulse vibrations similar to the artificial stimulus applied (Thompson, personal communication). The crabs' high degree of sensitivity, therefore, is unsurprising. Putting this result into an applicable environmental context would require measuring the scaling factor between signal propagation in the steel table and in the rocky substrate of the intertidal. This would allow direct quantification of crab sensitivity to impulse stimuli in their natural environment in terms of distance from the stimulus source. While testing the response of the leg receptors to various frequencies of vibrational stimuli, it was interesting to see changes in the nervous response pattern with the application of different frequency ranges. Lower frequency stimuli of 10-50 Hz elicited obvious phase-locking in the receptor output, but vibrations above 50 Hz did not. This may simply be due to constraints in the speed of successive action potentials resulting from intrinsic properties of the individual neurons. It may also, however, indicate that different neurons, with different output characteristics, are responding at the different frequencies. Due to limitations in the experimental methods, it was not possible to differentiate and record from single vibration¬ sensitive neurons within the leg nerve. Aicher and Tautz (1983), in experiments on the leg nerve of the fiddler crab Uca pugilator were able to identify two types of neurons in the walking-leg nerve which responded differently to sinusoidal mechanical stimuli. One of these neuron types was excited only in the frequency range of 2-100 Hz. While no assumptions are made here as to morphological similarities between these two crab species, this indicates the possibility that individual nerve fibers respond differently to the application of distinct stimulus frequency ranges. It is also possible that the different response patterns result from the activation of different vibration-sensitive receptors in the leg. The crab dactyl is covered with sensory hairs of different size, position and morphology (Fig 2), which may be differentially recruited to encode stimuli within specific frequency ranges. It is likely that different receptor populations synapse with different neuronal populations in order to allow signal discrimination. Differential stimulation of receptors, therefore, would activate different groups of neurons with distinct response patterns. To determine whether this is the case, it would be necessary to characterize the different leg receptors, and determine which are recruited to respond at each applied frequency It was interesting to note that none of the data analysis techniques pursued in this experiment demonstrated sensor responsiveness to vibration frequencies above 500 Hz. The normalized frequency response analysis actually indicated a significant decrease in nerve firing rate in response to the 1000 Hz stimulus level (Fig. 7). While this prevents a confident claim of responsiveness at greater frequencies, it seems premature to conclude that they do not lie within receptor sensitivity range. Related arthropods, such as the fiddler crab and the cricket (Aicher and Tautz 1983; Dusenbery 1992), have been demonstrated to respond to stimuli intensity up to 2 kHz. The decrease in firing observed in the Pachygrapsis leg nerve at these higher rates, therefore, may in fact be a form of sensory encoding through different neuronal response patterns. Aicher and Tautz found that the spontaneous activity of one of the fiddler crab leg nerve units was increasingly suppressed by the application of higher frequency stimuli. This inhibition was postulated to help in discrimination between low frequency communicational signals and high frequency background noise. Although vibrational communication is not an obvious behavioral characteristic of Pachygrapsis, inhibition could still play a role in signal discrimination in their natural habitat. Inhibition of this kind could easily account for the decrease in normalized response seen with the application of high frequency stimuli. Determining whether inhibition is a method of response would potentially allow more complete characterization of the vibration sensitivity range of the crab. Doing so, however, would require recording from individual neuronal fibers within the leg nerve to determine their distinct response characteristics which was beyond the scope of this particular experiment. Conclusions: This experiment made substantial strides in characterizing the frequency sensitivity range of the lined purple shore crab. Threshold sensitivity to high-frequency stimuli was determined, and responsiveness was demonstrated within a vibrational stimulus range of 10-500 Hz. The next critical step in a sensory ecology based analysis, is to move into the field and determine the vibrational stimuli naturally present in the animal’s informational channel. This would allow a clearer characterization of the role played by vibrational signals in ensuring the species' fitness. It would also allow a calculation of anthropogenic noise sources that could interfere with this sensitivity window and therefore disrupt the species' ability to relate maximally to its environment. Reference Aicher, B and J. Tautz. 1984. Peripheral inhibition' of vibration sensitive units in the leg of the fiddler crab Uca pugilator. J Comp Physiol A. 154: 42-49. Aicher, B and J. Tautz. 1990. Vibrational communication in the fiddler crab, Uca pugilator. J Comp Physiol A. 166: 345-353. Bowdan, E and G. Wyse. 1996. Sensory Ecology: Introduction. Biol Bull. 191: 122-123. Buno, W.; Crispino, L.; Monti-Bloch, L.; Mateos, A. 1981. Dynamic analysis of cockroach giant interneuron activity evoked by forced displacement of cercal thread-hair sensilla. Journal of Neurobiology. 12: 561-578. Buno, W.; Crispino, L.; Monti-Bloch, L. 1981. Dynamic properties of cockroach cercal "bristlelike" hair sensilla. Journal of Neurobiology. 12: 101-121. Dagan, D and J. Camhi. 1979. Responses to wind recorded from the cercal nerve of the cockroach Periplaneta americana. Comp Physiol. 133: 103-110. Dusenbery, D. 1992. Sensory Ecology. W. H. Freeman, New York, NY. Dusenbery, D. 1996. Information is where you find it. Biol. Bull. 191:124-128. Hall, J. 1985. Neuroanatomical and neurophysiological aspects of vibrational processing in the central nervous system of semi-terrestrial crabs. J Comp Physiol A. 157: 91-114. Manci, K.; Gladwin, D.; Villella, R.; Cavendish, M. 1988. The effects of aircraft noise and sonic booms on domestic animals and wildlife: a literature synthesis. U.S. Fish and Wildl. Serv. National Ecology Research Center, Ft. Collins, CO, NERC-88/29. 88 pp. Matsumoto, S.G. and R.K. Murphey. 1977. The cercus-to-giant interneuron system of crickets. Comp Physiol. 119: 319-330. Houck L, Drickamer L. 1996. Foundations of Animal Behavior. The University of Chicago Press, Chicago II. Salmon, M. 1971. Signal characteristics and acoustic detection by the fiddler crabs Uca rapax and Uca pugilator. Physiol Zoo.1 44: 210-224. Von Uexkull, J. 1957. A stroll through the world of animals and men. in C. Schiller, ed. Instinctive Behavior, International Universities Press, New York, NY. Wavelength, cm 34.4 344 Man Bats Rodents Whales and dolphins Seals and sea lions Birds Frogs Fish Moths Bush crickets Crickets Grasshoppers 0.5 0.02 0.05 0.1 0.2 Frequency, kHz Fig 1 3.44 0.344 10 20 50 100 0 m -2 rstesiskers indkagjdrs osegfsd skeg fenstegdeane seskensened flangste nendesdete k kta- 2 Tine (m) Sw 11 r r e øs pvren lgdaf vegnst ndesterte terede e e meso (B) Fig. 3 80. HE (*105 N-m) Approximate threshold Fig. 4 6 8 oo oo jelo A. o ooo elo ONI ) 8 o 8u I Autocorrelation Plot 1.C 0.5 -0.5 -1.0 100 200 300 400 500 60 (A) Lag Autocorrelation Plo 1.0 0.5 0.0 -0.5 -1. 100 200 300 400 500 60 (B) Lag Fig. 6 1.3 1.2 811 1 9 0.9 08 O.7 Normalized Frequency Response —4— Low amplitude —4— High amplitude 10 50 100 200 500 1000 stimulus frequency Fig. 7 Figure legend: Fig. 1. The hearing-frequency ranges of different animals. (Dusenbery 1992) Fig. 2. Scanning electron microscope pictures showing dactyl sensory hairs of different size and morphology. (A) Large, blunt cuticular hairs which are abundant and easily apparent to the naked eye (picture width approx. 1500 u) (B) Closer look at the junction between a large hair and the dactyl. Smaller sensory hairs are apparent in the lower right corner (width = 300 u) (C) Detail of a smaller sensory hair (width = 90 u) Fig. 3. Examples of stimulus data. The bottom trace of each recording is input from the strain gauge, and stimulus impact is marked by arrows. The top trace is the corresponding response from the crab leg nerve. The recordings are magnified to show 100 ms of the 1000 ms recording. (A) Stimulus force applied is 0.274 * 10-5 N-m. No response is apparent from the leg and the trial is given a binary score of 0. (B) Stimulus force is 1.92 * 10-5 N-m and a response is clearly seen. The trial is given a score of 1 Fig. 4. Diagram of approximate response threshold range using a binary scoring scheme. Some data points are obscured due to trials performed with the same degree of force application. It is clear, however, that below approximately 1.4 * 10-5 N-m responses are consistently scored as 0. Above 3.9 * 10-5 N-m they are consistently 1, Within this interval response is variable and is, therefore, designated as the threshold sensitivity range. Fig. 5. (A) Leg response train to 1Ohz stimulus and corresponding autocorrelation diagram. The arrows indicate regular bursts of action potentials phase-locked to stimulus frequency and, therefore, occurring every 10 ms. Phase-locking is confirmed by a significant correlation (ie. exceeds red criterion lines) at 400 lag units which corresponds to 10 ms. (B) 50 hz stimulus. Phase-locking occurs in 20 ms intervals and is less apparent in the response train. The correlation diagram indicates significant correlations every 80 lag units, or 20 ms. Fig. 6. (A) Autocorrelation data resulting from the application of 100 Hz. stimuli. If phase-locking is occurring in the output train, significant correlations should be observed every 40 lag points. (B) Autocorrelation data resulting from a control recording without stimulus applied. The two traces are nearly indistinguishable indicating that phase locking is no longer occurring at frequencies above 50 Hz. Fig.7. Diagram of averaged normalized response to applied stimulus frequencies. High and low amplitude stimulation at each frequency are plotted separately. The high amplitude trace consistently exceeds that of the low. A normalized response greater than 1 indicates an increase in temporal firing rate in response to stimulus application. This is c clearly seen in the stimulus frequency range of 50-200 Hz. 1000 Hz elicits a notable decrease in firing rate.