Evolutionary Ecology

, Volume 23, Issue 2, pp 245–259

Consequences of food distribution for optimal searching behavior: an evolutionary model

Original Paper

Abstract

Resource distribution can vary greatly in space and time. Consequently, animals should adjust their searching tactics to such spatio–temporal patterns in accordance with their innate capabilities, or alternatively, they should use a genetically fixed searching tactic that has been evolved in response to the specific pattern of the food they experience. Using a simulation model and a genetic algorithm, we show how optimal searching tactics change as a function of food spatial pattern. Searching tactics for hidden prey can be approximated using the following three components: (1) Extensive search mode (ESM), the type of movement before encountering a food item; (2) Intensive search mode (ISM), the type of movement after encountering a food item; and (3) ISM duration. Both ESM and ISM are characterized by movement tortuosity. We show that searching behavior adaptively changes as a function of food pattern. When food is distributed in a regular pattern, ISM is more directional than ESM, but under a clumped food pattern, ISM is much more tortuous than ESM. It may suggest that animals with larger spectra of searching tactics should experience greater variance or seasonal changes in their food pattern than animals with narrow spectra of searching tactics. Increased forager attack radius diminishes the differences between ESM and ISM, and thus the use of these three components to model searching in animals with higher attack radii is not appropriate. Increased handling time, which is a surrogate of reducing habitat profitability results in longer patch residency time as expected by optimal foraging theory. To conclude, we suggest that using such a combined approach of simulation models and genetic algorithms may improve our understanding of how extrinsic and intrinsic factors interact to influence searching behavior.

Keywords

Area-restricted search Attack radius Foraging Handling time Genetic algorithm Searching tactic 

References

  1. Baum KA, Grant WE (2001) Hummingbird foraging behavior in different patch types: simulation of alternative strategies. Ecol Model 137:201–209CrossRefGoogle Scholar
  2. Bell WJ (1985) Sources of information controlling motor patterns in arthropod local search orientation. J Insect Physiol 31:837–847CrossRefGoogle Scholar
  3. Benhamou S (1992) Efficiency of area-concentrated searching behaviour in a continuous patchy environment. J Theor Biol 159:67–81CrossRefGoogle Scholar
  4. Benhamou S (2004) How to reliably estimate the tortuosity of an animal’s path: straightness, sinuosity or fractal dimension? J Theor Biol 229:209–220PubMedCrossRefGoogle Scholar
  5. Ben-Natan G, Abramsky Z, Kotler BP, Brown JS (2004) Seeds redistribution in sand dunes: a basis for coexistence of two rodent species. Oikos 105:325–335CrossRefGoogle Scholar
  6. Bond AB (1980) Optimal foraging in a uniform habitat: the search mechanism of the green lacewing. Anim Behav 28:10–19CrossRefGoogle Scholar
  7. Broom M, Ruxton GD (2005) You can run––or you can hide: optimal strategies for cryptic prey against pursuit predators. Behav Ecol 16:534–540 CrossRefGoogle Scholar
  8. Bouskila A, Blumstein DT (1992) Rules of thumb for predation hazard assessment: predictions from a dynamic model. Am Nat 139:161–176CrossRefGoogle Scholar
  9. Carter MC, Dixon AFG (1984) Foraging behaviour of Coccinellid larvae: duration of intensive search. Entomol Exp Appl 36:133–136CrossRefGoogle Scholar
  10. Charnov EL (1976) Optimal foraging, the marginal value theorem. Theor Popul Biol 9:129–136PubMedCrossRefGoogle Scholar
  11. Compton SG (2001) Sailing with the wind: dispersal by small flying insects. In: Bullock JM, Kenward RE, Hails RS (eds) Dispersal ecology. Blackwell Publishing, Oxford, pp 113–133Google Scholar
  12. Dicke M, Burrough PA (1988) Using fractal dimensions for characterizing tortuosity of animal traits. Physiol Entomol 13:393–398CrossRefGoogle Scholar
  13. Ferran A, Dixon AFG (1993) Foraging behaviour of ladybird larvae (Coleoptera: Coccinellidae). Eur J Entomol 90:383–402Google Scholar
  14. Goldberg DE (1989) Genetic algorithms in search, optimization and machine-learning. Addison-Wesley, Reading, MAGoogle Scholar
  15. Griffiths D (1980) Foraging costs and relative prey size. Am Nat 116:734–752CrossRefGoogle Scholar
  16. Grimm V, Railsback SF (2005) Individual-based modeling and ecology. Princeton University Press, Princeton, NJGoogle Scholar
  17. Grünbaum D (1998) Using spatially explicit models to characterize foraging performance in heterogeneous landscapes. Am Nat 151:97–115PubMedCrossRefGoogle Scholar
  18. Hills T, Brockie PJ, Maricq AV (2004) Dopamine and glutamine control area-restricted search behavior in Caenorhabditis elegans. J Neurosci 24:1217–1225PubMedCrossRefGoogle Scholar
  19. Iwasa Y, Higashi M (1981) Prey distribution as a factor determining the choice of optimal foraging strategy. Am Nat 117:710–723CrossRefGoogle Scholar
  20. Kareiva P, Shigesada N (1983) Analyzing insect movement as a correlated random walk. Oecologia 56:234–238CrossRefGoogle Scholar
  21. Keller EF, Segel LA (1971) Model for chemotaxis. J Theor Biol 30:225–234PubMedCrossRefGoogle Scholar
  22. Krakauer DC, Rodriguez-Girones MA (1995) Searching and learning in a random environment. J Theor Biol 177:417–429CrossRefGoogle Scholar
  23. Mellgren RL, Roper TJ (1986) Spatial learning and discrimination of food patches in the European badger (Meles meles L.). Anim Behav 34:1129–1134CrossRefGoogle Scholar
  24. Meyers LA, Bull JJ (2002) Fighting change with change: adaptive variation in an uncertain world. Trends Ecol Evol 17:551–557CrossRefGoogle Scholar
  25. Mitchell M, Taylor CE (1999) Evolutionary computation: an overview. Ann Rev Ecol Syst 30:593–616CrossRefGoogle Scholar
  26. Motro U, Shmida A (1995) Near-far search: an evolutionary stable foraging strategy. J Theor Biol 173:15–22CrossRefGoogle Scholar
  27. Nakamuta K (1985) Mechanism of the switchover from extensive to area-concentrated search behaviour of the ladybird beetle, Coccintella septempunctata bruckii. J Insect Physiol 31:849–856CrossRefGoogle Scholar
  28. Nakamuta K (1987) Diel rhythmicity of prey-search activity and its predominance over starvation in the lady beetle, Coccinella septempunctata bruckii. Physiol Entomol 12:91–98CrossRefGoogle Scholar
  29. Nolet BA, Mooij WM (2002) Search paths of swans foraging on spatially autocorrelated tubers. J Anim Ecol 71:451–462CrossRefGoogle Scholar
  30. Ovadia O, Ziv Y, Abramsky Z, Pinshow B, Kotler BP (2001) Harvest rates and foraging strategies in Negev Desert gerbils. Behav Ecol 12:219–226CrossRefGoogle Scholar
  31. Ritchie ME (1998) Scale-dependent foraging and patch choice in fractal environments. Evol Ecol 12:309–330CrossRefGoogle Scholar
  32. Viswanathan GM, Buldyrev SV, Havlin S, da Luz MGE, Raposo EP, Stanley HE (1999) Optimizing the success of random searches. Nature 401: 911–914PubMedCrossRefGoogle Scholar
  33. Ward D, Saltz D (1994) Foraging at different spatial scales: Dorcase gazelles foraging for lilies in the Negev desert. Ecology 75: 48–58CrossRefGoogle Scholar
  34. Zollner PA, Lima SL (1999) Search strategies for landscape-level interpatch movements. Ecology 80:1019–1030CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Life SciencesBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Mitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevMidreshet Ben-GurionIsrael

Personalised recommendations