The need for speed
Limits on the speed advantage
Communication between its different regions is a key need for any living organism. However "the faster the better" is a bit overstated -- many communication functions do not have as much time urgency as those mentioned above, Various costs or other impediments to speed may constrain its evolution or ubiquity and thus must be figured into the analysis of factors governing its evolution. For example, swimming behavior ("tail flips") in crayfish in response to relatively weak or less sudden stimuli is mediated by a low conduction-speed network of neurons, giving reaction times in the order of 100 ms or more, allowing more time to refine an escape. Tail flips to more severe or sudden stimuli are initiated in 4-5 ms by a rapid-conduction network (Wine and Krasne, 1972). Thus even in the same organism the urgency of the signal received determines the speed of the conduction system used to respond. In other cases, conduction speed may be programmed for a deliberate pace according to need. In heart muscle, especially of advanced vertebrates, the sequential contraction of the different chambers is regulated by differences in relatively slow impulse conduction velocities along different parts of the muscular network (e.g. Klabunde, 2013; Milnor 1968a,b Table I). Speed per se is not the whole story in its evolution. Further, the "need for speed" can in some cases be reduced by localizing sensor and effectors close to each other so the communication distance is reduced, as when a fly trap closes in response to contact with tactile hairs located on the trap itself or the rapid jaw closure in trap-jaw ants triggered by sensory hairs located on the jaw (Gornenberg 1996). The whole length of the organism need not be involved. The centralization of nervous systems in evolution has thus increased the premium placed on speed, although regionalization of reaction circuits as in segment-oriented reflex organization remains valuable as a time-saver.
Bullock (1996) raised the possibility of a lower limit to the size of organisms for which morphologies usually thought to have evolved for promoting speed may be misleading: "The meaning of the giant fiber diameter cannot always be its greater velocity, for in small animals like Drosophila the absolute saving in time is small." Indeed, animals even as small as the 2.5 mm Drosophila (Tanouye and Wyman, 1980) or the 2 mm calanoid copepod (Lowe, 1935; Park 1966) possess giant fibers in rapid-response circuits. This raises the question of how fast is "fast enough"? One might consider what an upper limit might be on acceptable body-length transit times for rapid reactions. If we base this on transit times for fast-conducting mammalian axons over a 1 m body, impulses conducting at ~100 m s-1 would transit in ca. 10 ms. Wang et al. (2008) determined that brain-spanning delays in mammalian corpus callosum in the fastest axons were relatively independent of brain size, running between 1 and 2 ms for animals under a meter in length, which is equivalent to 10-20 ms body-length transit times. Such transit times would be generous for smaller organisms living on shorter time scales), although how to scale time for them is not obvious. A 10 m diameter mid-range Drosophila giant axon with HH squid parameters conducting at around 2 m s-1 (scaling for temperature and ionic strength), would take about 1 ms to cover one body length (comparable to observed delays: Tanouye and King, 1983). For average-sized axons 1/10 the diameter of the giants, the delay would be 3 ms, which would be three time the conduction delay in generating an escape and three-fold greater errors in conduction-dependent timing (e.g. Wang et al., 2008). It is not unlikely that such a large relative saving is significant for an organism living at the much faster time scale their size dictates. The 'easy solution' for speed enhancement, enlarging the axon, works for organisms large and small, but it has a cost in terms of space and energy consumption.
Conduction speed matched to behavioral needs
To put conduction speed in the right context, one needs to ask "how fast is required?" The rate at which an organism encounters new features in its environment to which it may need to react -- predators, resources, mates, etc., -- is key. This depends both on how fast its own movements are as well as those of relevant external objects. While the absolute rates of these vary greatly going from bacteria to whales, the two rates tend to be linked in order of magnitude (sedentary organisms excepted). An organism's communication system must operate at least at this same rate, i.e. the speed of the organism itself plus a safety margin. In detail, this is an involved subject but it provides a rough quantitative estimate for minimum acceptable conduction speeds. In general locomotion rates for an organism tend to increase with body size (a 2/3-power allometric relation to length for a given body plan is typical: Domenici and Blake,1997; Lenz et al., 2004; Sinervo and Huey 1990). On average also, organismal body size has progressively increased throughout evolutionary history (known as "Cope's Rule": e.g. Heim et al., 2015). Size increases give advantages in immunity from predation and access to resources, albeit the factors in the observed trend are still under active investigation (e.g. Kingsolver and Pfennig, 2004). Cope's rule puts additional pressure on communication speed, including steadily raising the bar for minimum acceptable conduction speed. But it could at the same time produce a "Red Queen" situation, since increasing speeds enable further body enlargement, which would lead to ever-increasing needs for speed just to keep up with other selective forces that enlarge bodies. Thus the issue of time scales and the need for speed is complex and deserving careful context-sensitive consideration.
While allometric scaling of sustained speeds have relied on metabolic cost calculations (e.g. Hedenstrom 2003), "burst speed" -- the peak velocity an animal can achieve in all-out locomotion -- is more appropriate for providing an index for rapid-conduction needs. Among aquatic organisms, Domenici and Blake (1997) found that a 2/3-power allometric relation to length for the maximum burst speeds of fish (vmax = 45.3L0.68 with length in mm and time in seconds) applies over an order of magnitude of length, or more. Lenz et al. (2004) extended this to a remarkable variety of other aquatic organisms including cladocerans and reptantians (crayfish and lobsters), molluscs (squid), aquatic insect and frog (tadpoles) larvae. Interestingly, they found that shrimp and copepods, while still obeying the L2/3 law, were respectively half and a full order of magnitude faster than comparably-sized fish and other main-stream aquatic organisms. Among terrestrial organisms, Sinervo and Huey (1990) conducted an ingenious experiment in which they created lizards of different sizes of the same species and developmental stage by withdrawing various amounts of yolk from incubating eggs. The depleted eggs produced isometrically-scaled individuals of a variety of sizes. The sprint speeds of these different-sized individuals also obeyed a power law with a mass-specific allometric index of 0.242, corresponding to a body-length scaling of L0.73, not too different from L0.67. Applying the approximate time scale argument, we might conclude that life history time scales approximately as L2/3. For standard-curve aquatic organisms, that gives 45 mm s-1 for a 1 mm animal or a time-scale for moving 1 body length (BL) of 22 ms. A copepod moving at 10 times this speed travels 1 BL in 2 ms. A nerve impulse in a giant axon of a 1 mm individual has been estimated by Lenz et al. (2004) to be around 1.5 m s-1, so the approximate transit time along the body would be around 0.7 ms, which is a "safety" factor of ca. 3 -- adequate by this criterion, but none too large.
Alexander, R.M. (2003) Principles of Animal Locomotion Princeton Univ. Press 371pp
Bullock, T.H., 1996. Neuroethology of zooplankton, in: Lenz, P.H., Hartline, D.K., Purcell, J.E., Macmillan, D.A. (Eds.), Zooplankton: Sensory Ecology and Physiology. Gordon and Breach, Amsterdam, pp. 1-16.
Domenici, P., Blake, R.W., 1997. The kinematics and performance of fish fast-start swimming. J. Exp. Biol. 200, 1165-1178
Gronenberg, W. 1996. The trap-jaw mechanism in the dacetine ants Daceton armigerum and Strumigenys sp. J. Exp. Biol. 199:2021-2033
Heim, N.A., Knope, M.L., Schaal, E.K., Wang, S.C., Payne, J.L., 2015. Cope's rule in the evolution of marine animals. Science. 347, 867-870.
Hedenström, A. 2003. Scaling migration speed in animals that run, swim and fly. J. Zool. Lond. 259: 155-160.
Kingsolver, J.G., Pfennig, D.W., 2004. Individual?level selection as a cause of cope's rule of phyletic size increase. Evolution 58(7)1608-1612.
Klabunde RE Cardiovascular Physiology Concepts downloaded 2015-06-11
Lenz, P.H., Hower, A.E., Hartline, D.K., 2004. Force production during pereiopod power strokes in Calanus finmarchicus. J. Mar. Sys. 49, 133-144.
Lowe, E., 1935. On the anatomy of a marine copepod, Calanus finmarchicus (Gunnerus). Trans. Roy. Soc. Edin. 63, 560-603.
Milnor, W.R. 1968a. Cardiovascular system. In Mountcastle V.B., ed. Medical Physiology vol. 1 (12th edition) C.V. Mosby Co., St. Louis. pp 24-34.
Milnor, W.R. 1968b. Normal circulatory function. In Mountcastle V.B., ed. Medical Physiology vol. 1 (12th edition) C.V. Mosby Co., St. Louis. pp 118-133
Park, T.S., 1966. The biology of a calanoid copepod Epilabidocera amphitrites McMurrich. Cellule 66, 129-251.
* Sinervo B., Huey, R.B., 1990. Allometric engineering: An experimental test of the causes of interpopulational differences in performance. Science 248, 1106-1109.
Tanouye, M.A., King, D.G., 1983. Giant fibre activation of direct flight muscles in Drosophila. J. Exp. Biol. 105, 241-251.
Tanouye, M.A., Wyman, R.J., 1980. Motor outputs of giant nerve fiber in Drosophila. J. Neurophysiol. 44(2), 405-421.
Wine, J.J., Krasne, F.B., 1972. The organization of escape behaviour in the crayfish. J. Exp. Biol. 56, 1-18.
This material has been assembled and presented as a public service by Dan Hartline, Bekesy Laboratory of Neurobiology, Pacific Biosciences Research Center, University of Hawaii at Manoa (danh at hawaii.edu). Opinions expressed here are those of the author and do not represent the position or policies of the University or any funding agency.
Return to Myelin Evolution home page