, Volume 174, Issue 3, pp 723–729 | Cite as

Experimental determination of the spatial scale of a prey patch from the predator’s perspective

  • Matthew A. BirkEmail author
  • J. Wilson White
Behavioral ecology - Original research


Foraging theory predicts that predators should prefer foraging in habitat patches with higher prey densities. However, density depends on the spatial scale at which a “patch” is defined by an observer. Ecologists strive to measure prey densities at the same scale that predators do, but many natural landscapes lack obvious, well-defined prey patches. Thus one must determine the scale at which predators define patches of prey. We estimated the scale at which guppies, Poecilia reticulata, selected patches of zooplankton prey using a behavioral assay. Guppies could choose between two prey arrays, each manipulated to have a density that depended on the spatial scale at which density was calculated. We estimated the scale of guppy foraging by comparing guppy preferences across a series of trials in which we systematically varied the scale associated with “high” prey density. This approach enables the application of foraging theory to non-discrete habitats and prey landscapes.


Foraging theory Patch selection Poecilia reticulata Density-dependent mortality Scale dependence 



The authors would like to thank Matt Kon and Joanna Lewis for assistance with animal care. M. A. Birk would also like to thank Dr. Fredrick Scharf and Dr. Mark Galizio for being committee members of this thesis and Jesus Christ for this opportunity. This study was funded by the UNCW Department of Biology and Marine Biology and a UNCW Center for the Support of Undergraduate Research and Fellowships Research Supplies Grant. All research was conducted in accordance with all applicable laws and rules set forth by the USA and the UNCW Institutional Animal Care and Use Committee.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

442_2013_2818_MOESM1_ESM.docx (70 kb)
Supplementary material 1 (DOCX 70 kb)


  1. Abrahams MV (1989) Foraging guppies and the ideal free distribution: the influence of information on patch choice. Ethol 82:116–126Google Scholar
  2. Bates D, Maechler M, Bolker B (2012) lme4: linear mixed-effects models using S4 classes. R package version 0.999999-0.
  3. Bax NJ (1998) The significance and prediction of predation in marine fisheries. ICES J Mar Sci 55:997–1030CrossRefGoogle Scholar
  4. Bergman CM, Fryxell JM, Gates CC, Fortin D (2001) Ungulate foraging strategies: energy maximizing or time minimizing? J Anim Ecol 70:289–300CrossRefGoogle Scholar
  5. Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White JSS (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 24:127–135PubMedCrossRefGoogle 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. Burger AE, Hitchcock CL, Davoren GK (2004) Spatial aggregations of seabirds and their prey on the continental shelf off SW Vancouver Island. Mar Ecol Prog Ser 283:279–292CrossRefGoogle Scholar
  8. Cowie RJ (1977) Optimal foraging in great tits (Parus major). Nature 268:137–139CrossRefGoogle Scholar
  9. Cummings VJ, Schneider DC, Wilkinson MR (1997) Multiscale experimental analysis of aggregative responses of mobile predators to infaunal prey. J Exp Mar Biol Ecol 216:211–227. doi: 10.1016/S0022-0981(97)00097-X CrossRefGoogle Scholar
  10. Day RL, MacDonald T, Brown C et al (2001) Interactions between shoal size and conformity in guppy social foraging. Anim Behav 62:917–925CrossRefGoogle Scholar
  11. Elliott NC, Kieckhefer RW (2000) Response by coccinellids to spatial variation in cereal aphid density. Popul Ecol 42:81–90CrossRefGoogle Scholar
  12. Emlen JM (1966) The role of time and energy in food preference. Am Nat 100:611–617CrossRefGoogle Scholar
  13. Fisher RA (1932) Statistical methods for research workers, 4th edn. Oliver and Boyd, EdinburghGoogle Scholar
  14. Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, CambridgeGoogle Scholar
  15. Gelman A, Su Y-S (2013) arm: data analysis using regression and multilevel/hierarchical models. R package version 1.6-04.
  16. Hurlbert SH, Lombardi CM (2009) Final collapse of the Neyman-Pearson decision theoretic framework and rise of the neoFisherian. Ann Zool Fenn 46:311–349CrossRefGoogle Scholar
  17. Ioannou CC, Krause J (2008) Searching for prey: the effects of group size and number. Anim Behav 75:1383–1388CrossRefGoogle Scholar
  18. Ioannou CC, Morrell LJ, Ruxton GD, Krause J (2009) The effect of prey density on predators: conspicuousness and attack success are sensitive to spatial scale. Am Nat 173:499–506PubMedCrossRefGoogle Scholar
  19. Johnson CJ, Parker KL, Heard DC (2001) Foraging across a variable landscape: behavioral decisions made by woodland caribou at multiple spatial scales. Oecologia 127:590–602CrossRefGoogle Scholar
  20. Krebs JR, Davies NB (1986) Behavioural ecology. Blackwell Science, SussexGoogle Scholar
  21. Krebs JR, Ryan JC, Charnov EL (1974) Hunting by expectation or optimal foraging? A study of patch use by chickadees. Anim Behav 22:953–964CrossRefGoogle Scholar
  22. Lachmann M, Sell G, Jablonka E (2000) On the advantages of information sharing. Proc R Soc Lond B 267:1287–1293CrossRefGoogle Scholar
  23. Landeau L, Terborgh J (1986) Oddity and the ‘confusion effect’ in predation. Anim Behav 34:1372–1380CrossRefGoogle Scholar
  24. Levin SA (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur Award Lecture. Ecology 73:1943–1967CrossRefGoogle Scholar
  25. Lima SL (2002) Putting predators back into behavioral predator–prey interactions. Trends in Ecol Evol 17:70–75CrossRefGoogle Scholar
  26. MacArthur RH, Pianka ER (1966) On optimal use of a patchy environment. Am Nat 100:603–609CrossRefGoogle Scholar
  27. Mangel M, Clark CW (1986) Towards a unified foraging theory. Ecology 67:1127–1138CrossRefGoogle Scholar
  28. Mohd Norowi H, Perry JN, Powell W, Rennolls K (2000) The effect of spatial scale on interactions between two weevils and their parasitoid. Ecol Entomol 25:188–196CrossRefGoogle Scholar
  29. Osborne JL, Martin AP, Carreck NL et al (2008) Bumblebee flight distances in relation to the forage landscape. J Anim Ecol 77:406–415PubMedCrossRefGoogle Scholar
  30. Osenberg CW, St. Mary CM, Schmitt RJ et al (2002) Rethinking ecological inference: density dependence in reef fishes. Ecol Lett 5:715–721CrossRefGoogle Scholar
  31. Overholtzer-McLeod KL (2006) Consequences of patch reef spacing for density-dependent mortality of coral-reef fishes. Ecology 87:1017–1026PubMedCrossRefGoogle Scholar
  32. Pitcher TJ, Parrish JK (1993) Functions of shoaling behavior in teleosts. In: Pitcher TJ (ed) Behaviour of teleost fishes. Chapman and Hall, London, pp 363–439CrossRefGoogle Scholar
  33. Pyke GH (1978) Optimal foraging: movement patterns of bumblebees between inflorescences. Theor Popul Biol 13:72–98PubMedCrossRefGoogle Scholar
  34. Pyke GH, Pulliam HR, Charnov EL (1977) Optimal foraging: a selective review of theory and tests. Q Rev Biol 52:137–154CrossRefGoogle Scholar
  35. Ray C, Hastings A (1996) Density dependence: are we searching at the wrong spatial scale? J Anim Ecol 65:556–566CrossRefGoogle Scholar
  36. R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  37. Schellhorn NA, Andow DA (2005) Response of coccinellids to their aphid prey at different spatial scales. Popul Ecol 47:71–76CrossRefGoogle Scholar
  38. Schneider DC, Piatt JF (1986) Scale-dependent correlation of seabirds with schooling fish in a coastal ecosystem. Mar Ecol Prog Ser 32:237–246CrossRefGoogle Scholar
  39. Smith JNM, Sweatman HPA (1974) Food-searching behavior of titmice in patchy environments. Ecology 55:1216–1232CrossRefGoogle Scholar
  40. Stephens DW, Krebs JR (1986) Foraging theory. Princeton University Press, New JerseyGoogle Scholar
  41. Stiling P, Throckmorton A, Silvanima J, Strong DR (1991) Does spatial scale affect the incidence of density dependence? A field test with insect parasitoids. Ecology 72:2143–2154CrossRefGoogle Scholar
  42. Swaney W, Kendal J, Capon H et al (2001) Familiarity facilitates social learning of foraging behaviour in the guppy. Anim Behav 62:591–598CrossRefGoogle Scholar
  43. Townsend CR, Winfield IJ (1985) The application of optimal foraging theory to feeding behavior in fish. In: Fish energetics. Springer, the Netherlands, pp 67–98Google Scholar
  44. Turchin P, Kareiva P (1989) Aggregation in Aphis varians: an effective strategy for reducing predation risk. Ecology 70:1008–1016CrossRefGoogle Scholar
  45. Waage JK (1979) Foraging for patchily-distributed hosts by the parasitoid, Nemeritis canescens. J Anim Ecol 48:353–371CrossRefGoogle Scholar
  46. Wellenreuther M, Connell SD (2002) Response of predators to prey abundance: separating the effects of prey density and patch size. J Exp Mar Biol Ecol 273:61–71Google Scholar
  47. Werner EE, Hall DJ (1974) Optimal foraging and the size selection of prey by the bluegill sunfish (Lepomis macrochirus). Ecology 55:1042–1052CrossRefGoogle Scholar
  48. White JW (2011) Can inverse density dependence at small spatial scales produce dynamic instability in animal populations? Theor Ecol 4:357–370CrossRefGoogle Scholar
  49. White JW, Warner RR (2007) Safety in numbers and the spatial scaling of density-dependent mortality in a coral reef fish. Ecology 88:3044–3054PubMedCrossRefGoogle Scholar
  50. White JW, Samhouri JF, Stier AC, Wormald CL, Hamilton SL, Sandin SA (2010) Synthesizing mechanisms of density dependence in reef fishes: behavior, habitat configuration, and observational scale. Ecology 91:1949–1961Google Scholar
  51. Wrona FJ, Dixon RWJ (1991) Group size and predation risk: a field analysis of encounter and dilution effects. Am Nat 137:186–201CrossRefGoogle Scholar
  52. Zach RJ, Falls JB (1976) Ovenbird (Aves: Parulidae) hunting behavior in a patchy environment: an experimental study. Can J Zool 54:1863–1879CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Biology and Marine BiologyUniversity of North Carolina WilmingtonWilmingtonUSA

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