DOI QR코드

DOI QR Code

Integration of Optimality, Neural Networks, and Physiology for Field Studies of the Evolution of Visually-elicited Escape Behaviors of Orthoptera: A Minireview and Prospects

  • Shin, Hong-Sup (Laboratory of Behavioral Ecology and Evolution, School of Biological Sciences, Seoul National University) ;
  • Jablonski, Piotr G. (Laboratory of Behavioral Ecology and Evolution, School of Biological Sciences, Seoul National University)
  • 발행 : 2008.05.30

초록

Sensing the approach of a predator is critical to the survival of prey, especially when the prey has no choice but to escape at a precisely timed moment. Escape behavior has been approached from both proximate and ultimate perspectives. On the proximate level, empirical research about electrophysiological mechanisms for detecting predators has focused on vision, an important modality that helps prey to sense approaching danger. Studies of looming-sensitive neurons in locusts are a good example of how the selective sensitivity of nervous systems towards specific targets, especially approaching objects, has been understood and realistically modeled in software and robotic systems. On the ultimate level, general optimality models have provided an evolutionary framework by considering costs and benefits of visually elicited escape responses. A recent paper showed how neural network models can be used to understand the evolution of visually mediated antipredatory behaviors. We discuss this new trend towards integration of these relatively disparate approaches, the proximate and the ultimate perspectives, for understanding of the evolution of behavior of predators and prey. Focusing on one of the best-studied escape pathway models, the Orthopteran LGMD/DCMD pathway, we discuss how ultimate-level optimality modeling can be integrated with proximate-level studies of escape behaviors in animals.

키워드

참고문헌

  1. Beddington JR, Free CA, Lawton JH. 1975. Dynamic complexity in predator-prey models framed in difference equations. Nature 255: 58-60 https://doi.org/10.1038/255058a0
  2. Berryman A. 1992. The origins and evolution of predator-prey theory. Ecology 73: 1530-1535 https://doi.org/10.2307/1940005
  3. Blanchard M, Rind FC, Verschurea PFMJ. 2000. Collision avoidance using a model of the locust LGMD neuron. Robot Auton Syst 30: 17-38 https://doi.org/10.1016/S0921-8890(99)00063-9
  4. Blumstein DT. 2003. Flight-initiation distance in birds is dependent on intruder starting distance. J Wildl Manag 67: 852-857 https://doi.org/10.2307/3802692
  5. Blumstein DT, Botton A, DaVeiga J. 2006. How does the presence of predators influence the persistence of antipredator behavior? J Theor Biol 239: 460-468 https://doi.org/10.1016/j.jtbi.2005.08.011
  6. Broom M, Ruxton GD. 2005. You can run-or you can hide: Optimal strategies for cryptic prey. Behav Ecol 16: 534-540 https://doi.org/10.1093/beheco/ari024
  7. Burrows M. 1996. The Neurobiology of an Insect Brain. Oxford University Press, New York
  8. Burrows M, Rowell CHF. 1973. Connections between descending visual interneurons and metathoracic motoneurons in the locust. J Comp Physiol 85: 221-234 https://doi.org/10.1007/BF00694231
  9. Cooper Jr. WE. 2006. Risk factors and escape strategy in the grasshopper Dissosteira carolina. Behaviour 143: 1201-1218 https://doi.org/10.1163/156853906778691595
  10. Cooper Jr. WE, Frederick WG. 2007. Optimal flight initiation distance. J Theor Biol 244: 59-67 https://doi.org/10.1016/j.jtbi.2006.07.011
  11. Dumont JPC, Robertson M. 1986. Neuronal circuits: An evolutionary perspective. Science 233: 849-853 https://doi.org/10.1126/science.233.4766.849
  12. Fouad K, Libersat F, Rathmayer W. 1996. Neuromodulation of the escape behavior of the cockroach Periplaneta americana by the venom of the parasitic wasp Ampulex compressa. J Comp Physiol A 178: 91-100
  13. Fullard HF, Yack JY. 1993. The evolutionary biology of insect hearing. Trends Ecol Evol 8: 248-252 https://doi.org/10.1016/0169-5347(93)90200-9
  14. Gabbiani F, Krapp HG, Koch C, Laurent G. 2002. Multiplicative computation in a visual neuron sensitive to looming. Nature 420: 320-324 https://doi.org/10.1038/nature01190
  15. Gabbiani F, Laurent G, Hatsopoulos N, Krapp HG. 1999. The many ways of building collision-sensitive neurons. Trends Neurosci 22: 437-438 https://doi.org/10.1016/S0166-2236(99)01478-2
  16. Gahtan E, Sankrithi N, Campos JB, O'Malley DM. 2002. Evidence for a widespread brain stem escape network in larval zebrafish. J Neurophysiol 87: 608-614 https://doi.org/10.1152/jn.00596.2001
  17. Gray JR. 2005. Habituated visual neurons in locusts remain sensitive to novel looming objects. J Exp Biol 208: 2515-2532 https://doi.org/10.1242/jeb.01640
  18. Hale ME, Long Jr. JH, McHenry MJ, Westneat MW. 2002. Evolution of behavior and neural control of the fast-start escape response. Evolution 56: 993-1007 https://doi.org/10.1111/j.0014-3820.2002.tb01411.x
  19. Hatsopoulos N, Gabbiani F, Laurent G. 1995. Elementary computation of object approach by a wide-field visual neuron. Science 270: 1000-1003 https://doi.org/10.1126/science.270.5238.1000
  20. Holmqvist MH, Srinivasan MV. 1991. A visually evoked escape response of the housefly. J Comp Physiol A 169: 451-459
  21. Jabłoński PG, Strausfeld N. 2000. Exploitation of an ancient escape circuit by an avian predator: prey sensitivity to model predator display in the field. Brain Behav Evol 56: 94-106 https://doi.org/10.1159/000006680
  22. Konish M. 1986. Centrally synthesized maps of sensory space. Trends Neurosci 9: 163-168 https://doi.org/10.1016/0166-2236(86)90053-6
  23. Magnhagen C. 1991. Predation risk as a cost of reproduction. Trends Ecol Evol 6: 183-186 https://doi.org/10.1016/0169-5347(91)90210-O
  24. Marler P. 1991. Song-learning behavior: The interface with neuroethology. Trends Neurosci 14: 199-205 https://doi.org/10.1016/0166-2236(91)90106-5
  25. Medan V, Oliva D, Tomsic D. 2007. Characterization of lobula giant neurons responsive to visual stimuli that elicit escape behaviors in the Crab Chasmagnathus. J Neurophysiol 98: 2414-2428 https://doi.org/10.1152/jn.00803.2007
  26. Oliva D, Medan V, Tomsic D. 2007. Escape behavior and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). J Exp Biol 210: 865-880 https://doi.org/10.1242/jeb.02707
  27. O'Shea M, Rowell CHF. 1976. The neuronal basis of a sensory analyzer, the acridid movement detector system. J Exp Biol 65: 289- 308
  28. O'Shea M, Williams JLD. 1974. The anatomy and output connections of a locust visual interneurone: the lobular giant movement detector (LGMD) neurone. J Comp Physiol 91: 257-266 https://doi.org/10.1007/BF00698057
  29. Rind FC, Bramwell DI. 1996. Neural network based on the input organization of an identified neuron signaling impending collision. J Neurophysiol 75: 967-985 https://doi.org/10.1152/jn.1996.75.3.967
  30. Rind FC, Santer RD. 2004. Collision avoidance and a looming sensitive neuron: Size matters but biggest is not necessarily best. Proc R Soc Lond B 271: S27-S29
  31. Rind FC, Simmons PJ. 1992. Orthopteran DCMD neuron: A reevaluation of responses to moving objects. I. Selective responses to approaching objects. J Neurophysiol 68: 1654-1666 https://doi.org/10.1152/jn.1992.68.5.1654
  32. Rind FC, Simmons PJ. 1999. Seeing what is coming: building collision- sensitive neurons. Trends Neurosci 22: 215-220 https://doi.org/10.1016/S0166-2236(98)01332-0
  33. Rowell CHF. 1971. The orthopteran descending movement detector (DMD) neurones: a characterisation and review. 2. Vgl Physiol 73: 167-194 https://doi.org/10.1007/BF00304131
  34. Santer RD, Simmons PJ, Rind FC. 2005. Gliding behaviour elicited by lateral looming stimuli in flying locusts. J Comp Physiol A 191: 61-73 https://doi.org/10.1007/s00359-004-0572-x
  35. Shepherd GM. 1988. Neurobiology. Oxford University Press, New York
  36. Simmons PJ, Rind FC. 1992. Orthopteran DCMD neuron: a reevaluation of responses to moving objects. II. Critical cues for detecting approaching objects. J Neurophysiol 68: 1667-1682 https://doi.org/10.1152/jn.1992.68.5.1667
  37. Stafford R, Santer RD, Rind FC. 2007. A bio-inspired visual collision detection mechanism for cars: Combining insect inspired neurons to create a robust system. Biosystems 87: 164-171 https://doi.org/10.1016/j.biosystems.2006.09.010
  38. Stafford R, Santer RD, Rind FC. 2007. The role of behavioural ecology in the design of bio-inspired technology. Anim Behav 74: 1813- 1819 https://doi.org/10.1016/j.anbehav.2007.07.015
  39. Trimarchi JR, Schneiderman AM. 1993. Giant fiber activation of an intrinsic muscle in the mesothoracic leg of Drosophila melanogaster. J Exp Biol 177: 149-167
  40. Wu JC, Popović Z. 2003. Realistic modeling of bird flight animations. In Proceedings of SIGGRAPH. 2003: 888-895
  41. Ydenberg RC, Dill LM. 1986. The economics of fleeing from predators. Adv Study Behav 16: 229-249 https://doi.org/10.1016/S0065-3454(08)60192-8
  42. Yoshida T, Jones LE, Ellner SP, Fussmann GF, Hairston Jr. NG. 2003. Rapid evolution drives ecological dynamics in a predator-prey system. Nature 424: 303-306 https://doi.org/10.1038/nature01767
  43. Zucker RS. 1972. Crayfish escape behavior and central synapses. I. Neural circuit exciting lateral giant fiber. J Neurophysiol 35: 599-620 https://doi.org/10.1152/jn.1972.35.5.599

피인용 문헌

  1. A review of escape behaviour in orthopterans vol.303, pp.3, 2017, https://doi.org/10.1111/jzo.12496