Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-05T05:13:56.497Z Has data issue: false hasContentIssue false
  • English
  • Français

Walking behaviour in the ground beetle, Poecilus cupreus: dispersal potential, intermittency and individual variation

Published online by Cambridge University Press:  30 September 2020

Joseph D. Bailey Open the ORCID record for Joseph D. Bailey [Opens in a new window] ,
Carly M. Benefer ,
Rod P. Blackshaw  and
Edward A. Codling
Joseph D. Bailey*
Affiliation:
Department of Mathematical Sciences, University of Essex, Colchester, CO4 3SQ, UK
Carly M. Benefer
Affiliation:
School of Biological and Marine Sciences, Plymouth University, Plymouth, PL4 8AA
Rod P. Blackshaw
Affiliation:
Blackshaw Research and Consultancy, Parade, Chudleigh, TQ13 0JF
Edward A. Codling
Affiliation:
Department of Mathematical Sciences, University of Essex, Colchester, CO4 3SQ, UK
*
Author for correspondence: Joseph D. Bailey, Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Dispersal is a key ecological process affecting community dynamics and the maintenance of populations. There is increasing awareness of the need to understand individual dispersal potential to better inform population-level dispersal, allowing more accurate models of the spread of invasive and beneficial insects, aiding crop and pest management strategies. Here, fine-scale movements of Poecilus cupreus, an important agricultural carabid predator, were recorded using a locomotion compensator and key movement characteristics were quantified. Net displacement increased more rapidly than predicted by a simple correlated random walk model with near ballistic behaviour observed. Individuals displayed a latent ability to head on a constant bearing for protracted time periods, despite no clear evidence of a population level global orientation bias. Intermittent bouts of movement and non-movement were observed, with both the frequency and duration of bouts of movement varying at the inter- and intra-individual level. Variation in movement behaviour was observed at both the inter- and intra- individual level. Analysis suggests that individuals have the potential to rapidly disperse over a wider area than predicted by simple movement models parametrised at the population level. This highlights the importance of considering the role of individual variation when analysing movement and attempting to predict dispersal distances.

Keywords

Dispersaldispersal potentialground beetlesintermittencypath analysisrandom walk
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 111 , Issue 2 , April 2021 , pp. 200 - 209
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahmed, DA, Petrovskii, SV and Tilles, PFC (2018) The “Lévy or Diffusion” controversy: how important is the movement pattern in the context of trapping? Mathematics 6, 77.CrossRefGoogle Scholar
Baird, E, Byrne, MJ, Smolka, J, Warrant, EJ and Dacke, M (2012) The dung beetle dance: an orientation behaviour? PLoS One 7, e30211.CrossRefGoogle Scholar
Banks, JE, Laubmeier, AN and Banks, HT (2020) Modelling the effects of field spatial scale and natural enemy colonization behaviour on pest suppression in diversified agroecosystems. Agricultural and Forest Entomology 22, 30–40.CrossRefGoogle Scholar
Bastola, A and Davis, JA (2018) Determining in-field dispersal of the redbanded stink bug (Hemiptera: pentatomidae) in soybean fields using a protein based mark-capture method. Crop Protection 112, 24–32.CrossRefGoogle Scholar
Benjamin, R, Cédric, G and Pablo, I (2008) Modeling spatially explicit population dynamics of Pterostichus Melanarius (Coleoptera: Carabidae) in response to changes in the composition and configuration of agricultural landscapes. Landscape and Urban Planning 84, 191–199.CrossRefGoogle Scholar
Biegler, R (2000) Possible uses of path integration in animal navigation. Animal Learning & Behavior 28, 257–277.CrossRefGoogle Scholar
Bohan, DA, Boursault, A, Brooks, DR and Petit, S (2011) National-scale regulation of the weed seedbank by carabid predators. Journal of Applied Ecology 48, 888–898.CrossRefGoogle Scholar
Brommer, JE (2013) On between-individual and residual (co) variances in the study of animal personality: are you willing to take the “individual gambit”? Behavioral Ecology and Sociobiology 67, 1027–1032.CrossRefGoogle Scholar
Byers, JA (2001) Correlated random walk equations of animal dispersal resolved by simulation. Ecology 82, 1680–1690.CrossRefGoogle Scholar
Byrne, M, Dacke, M, Nordström, P, Scholtz, C and Warrant, E (2003) Visual cues used by ball-rolling dung beetles for orientation. Journal of Comparative Physiology A 189, 411–418.CrossRefGoogle ScholarPubMed
Carpenter, SR (1996) Microcosm experiments have limited relevance for community and ecosystem ecology. Ecology 77, 677–680.CrossRefGoogle Scholar
Cheung, A, Zhang, S, Stricker, C and Srinivasan, MV (2007) Animal navigation: the difficulty of moving in a straight line. Biological Cybernetics 97, 47–61.CrossRefGoogle Scholar
Chittka, L, Williams, NM, Rasmussen, H and Thomson, JD (1999) Navigation without vision: bumblebee orientation in complete darkness. Proceedings of the Royal Society of London. Series B: Biological Sciences 266, 45–50.CrossRefGoogle Scholar
Choules, JD and Petrovskii, S (2017) Which random walk is faster? Methods to compare different step length distributions in individual animal movement. Mathematical Modelling of Natural Phenomena 12, 22–45.CrossRefGoogle Scholar
Codling, EA, Plank, MJ and Benhamou, S (2008) Random walk models in biology. Journal of the Royal Society Interface 5, 813–834.CrossRefGoogle ScholarPubMed
Codling, EA, Bearon, RN and Thorn, GJ (2010) Diffusion about the mean drift location in a biased random walk. Ecology 91, 3106–3113.CrossRefGoogle Scholar
Crist, TO, Guertin, DS, Wiens, JA and Milne, BT (1992) Animal movement in heterogeneous landscapes: an experiment with Eleodes Beetles in shortgrass prairie. Functional Ecology 6, 536–544.CrossRefGoogle Scholar
Dacke, M, Byrne, MJ, Scholtz, CH and Warrant, EJ (2004) Lunar orientation in a beetle. Proceedings of the Royal Society of London. Series B: Biological Sciences 271, 361–365.CrossRefGoogle Scholar
Dahmen, H, Wahl, VL, Pfeffer, SE, Mallot, HA and Wittlinger, M (2017) Naturalistic path integration of Cataglyphis Desert ants on an air-cushioned lightweight spherical treadmill. Journal of Experimental Biology 220, 634–644.CrossRefGoogle Scholar
Dingemanse, NJ and Dochtermann, NA (2013) Quantifying individual variation in behaviour: mixed-effect modelling approaches. Journal of Animal Ecology 82, 39–54.CrossRefGoogle ScholarPubMed
Dosmann, AJ, Brooks, KC and Mateo, JM (2015) Within-individual correlations reveal link between a behavioral syndrome, condition, and cortisol in free-ranging belding's ground squirrels. Ethology 121, 125–134.CrossRefGoogle ScholarPubMed
Edwards, AM, Phillips, RA, Watkins, NW, Freeman, MP, Murphy, EJ, Afanasyev, V, Buldyrev, SV, Luz, MGE, Raposo, EP, Stanley, HE and Viswanathan, GM (2007) Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer. Nature 449, 1044–1048.CrossRefGoogle ScholarPubMed
Firle, S, Bommarco, R, Ekbom, B and Natiello, M (1998) The influence of movement and resting behavior on the range of three carabid beetles. Ecology 79, 2113–2122.CrossRefGoogle Scholar
Goodwin, BJ and Fahrig, L (2002) Effect of landscape structure on the movement behaviour of a specialized goldenrod beetle, Trirhabda borealis. Canadian Journal of Zoology 80, 24–35.CrossRefGoogle Scholar
Hadfield, JD (2010) MCMC Methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software 33, 1–22.CrossRefGoogle Scholar
Holland, JM, Hutchison, MAS, Smith, B and Aebischer, NJ (2006) A review of invertebrates and seed-bearing plants as food for farmland birds in Europe. Annals of Applied Biology 148, 49–71.CrossRefGoogle Scholar
Houslay, TM and Wilson, AJ (2017) Avoiding the misuse of BLUP in behavioural ecology. Behavioral Ecology 28, 948–952.CrossRefGoogle ScholarPubMed
Jopp, F and Reuter, H (2005) Dispersal of carabid beetles—emergence of distribution patterns. Ecological Modelling, Emergent Properties in Individual-based Models Case Studies from the Bornhöved Project (Northern Germany) 186, 389–405.Google Scholar
Kareiva, PM and Shigesada, N (1983) Analyzing insect movement as a correlated random walk. Oecologia 56, 234–238.CrossRefGoogle ScholarPubMed
Knell, AS and Codling, EA (2012) Classifying area-restricted search (ARS) using a partial sum approach. Theoretical Ecology 5, 325–339.CrossRefGoogle Scholar
Kramer, E (1976) The orientation of walking honeybees in odour fields with small concentration gradients. Physiological Entomology 1, 27–37.CrossRefGoogle Scholar
Kramer, DL and McLaughlin, RL (2001) The behavioral ecology of intermittent locomotion. American Zoologist 41, 137–153.Google Scholar
Kromp, B (1999) Carabid beetles in sustainable agriculture: a review on pest control efficacy, cultivation impacts and enhancement. Agriculture, Ecosystems & Environment 74, 187–228.CrossRefGoogle Scholar
Lessells, CM and Boag, PT (1987) Unrepeatable repeatabilities: a common mistake. The Auk 104, 116–121.CrossRefGoogle Scholar
Levins, R (1966) The strategy of model building in population biology. American scientist 54, 421–431.Google Scholar
Lövei, GL and Sunderland, KD (1996) Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annual Review of Entomology 41, 231–256.CrossRefGoogle Scholar
Lövei, GL, Stringer, IAN, Devine, CD and Cartellieri, M (1997) Harmonic radar - A method using inexpensive tags to study invertebrate movement on land. New Zealand Journal of Ecology 21, 187–193.Google Scholar
Luff, ML (1998) Provsional atlas of the ground beetles (Coleoptera, Carabidae) of Britain. Biological Records Centre, Huntingdon, UK.Google Scholar
Luff, ML (2002) Carabid assemblage organization and species composition. In Holland, JM (ed), The Agroecology of Carabid Beetles. Intercept, Andover, UK, pp. 41–80.Google Scholar
MacLeod, A, Wratten, SD, Sotherton, NW and Thomas, MB (2004) ‘Beetle banks’ as refuges for beneficial arthropods in farmland: long-term changes in predator communities and habitat. Agricultural and Forest Entomology 6, 147–154.CrossRefGoogle Scholar
Mardia, KV and Jupp, PE (2009) Directional statistics (Vol. 494). John Wiley & Sons, Chichester, UK.Google Scholar
Marsh, LM and Jones, RE (1988) The form and consequences of random walk movement models. Journal of Theoretical Biology 133, 113–131.CrossRefGoogle Scholar
Mashanova, A, Oliver, TH and Jansen, VA (2010) Evidence for intermittency and a truncated power law from highly resolved aphid movement data. Journal of the Royal Society Interface 7, 199–208.CrossRefGoogle Scholar
Morales, JM and Ellner, SP (2002) Scaling up animal movements in heterogeneous landscapes: the importance of behavior. Ecology 83, 2240–2247.CrossRefGoogle Scholar
Mundy, CA, Allen-Williams, LJ, Underwood, N and Warrington, S (2000) Prey selection and foraging behaviour by Pterostichus cupreus L. (Coleoptera: Carabidae) under laboratory conditions. Journal of Applied Entomology 124, 349–358.CrossRefGoogle Scholar
Nakagawa, S and Schielzeth, H (2010) Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85, 935–956.Google ScholarPubMed
Pearce, RF, Giuggioli, L and Rands, SA (2017) Bumblebees can discriminate between scent-marks deposited by conspecifics. Scientific Reports 7, 43872.CrossRefGoogle ScholarPubMed
Petrovskii, S, Petrovskaya, N and Bearup, D (2014) Multiscale approach to pest insect monitoring: random walks, pattern formation, synchronization, and networks. Physics of Life Reviews 11, 467–525.CrossRefGoogle ScholarPubMed
Pocock, MJO and Jennings, N (2007) Testing biotic indicator taxa: the sensitivity of insectivorous mammals and their prey to the intensification of lowland agriculture. Journal of Applied Ecology 45, 151–160.CrossRefGoogle Scholar
Rainio, J and Niemelä, J (2003) Ground beetles (Coleoptera: Carabidae) as bioindicators. Biodiversity and Conservation 12, 487–506.CrossRefGoogle Scholar
R Core Team (2018) R: A Language and Environment for Statistical Computing. Vienna, Austria, R Foundation for Statistical Computing. Available at http://www.R-project.org/.Google Scholar
Reynolds, AM, Leprêtre, L and Bohan, DA (2013) Movement patterns of Tenebrio Beetles demonstrate empirically that correlated-random-walks have similitude with a Lévy walk. Scientific Reports 3, 3158.CrossRefGoogle ScholarPubMed
Rijnsdorp, AD (1980) Pattern of movement in and dispersal from a Dutch Forest of Carabus Problematicus Hbst. (Coleoptera, Carabidae). Oecologia 45, 274–281.CrossRefGoogle Scholar
Růžičková, J and Veselý, M (2016) Using radio telemetry to track ground beetles: movement of Carabus ullrichii. Biologia 71, 924–930.CrossRefGoogle Scholar
Schindler, DW (1998) Whole-ecosystem experiments: replication versus realism: the need for ecosystem-scale experiments. Ecosystems 1, 323–334.CrossRefGoogle Scholar
Schwind, R (1991) Polarization vision in water insects and insects living on a moist substrate. Journal of Comparative Physiology A 169, 531–540.CrossRefGoogle Scholar
Sims, DW, Righton, D and Pitchford, JW (2007) Minimizing errors in identifying Lévy flight behaviour of organisms. Journal of Animal Ecology 76, 222–229.CrossRefGoogle ScholarPubMed
Souman, JL, Frissen, I, Sreenivasa, MN and Ernst, MO (2009) Walking straight into circles. Current Biology 19, 1538–1542.CrossRefGoogle ScholarPubMed
Srivastava, DS, Kolasa, J, Bengtsson, J, Gonzalez, A, Lawler, SP, Miller, TE, Munguia, P, Romanuk, T, Schneider, DC and Trzcinski, MK (2004) Are natural microcosms useful model systems for ecology? Trends in Ecology & Evolution 19, 379–384.CrossRefGoogle ScholarPubMed
Stoffel, MA, Nakagawa, S and Schielzeth, H (2017) Rptr: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods in Ecology and Evolution 8, 1639–1644.CrossRefGoogle Scholar
Syntech (2004) TrackSphere Locomotion Compensator model LC-300. User guide. Syntech Hilversum, The Netherlands.Google Scholar
Thomas, MB, Wratten, SD and Sotherton, NW (1991) Creation of “island” habitats in farmland to manipulate populations of beneficial arthropods: predator densities and emigration. Journal of Applied Ecology 28, 906–917.CrossRefGoogle Scholar
Thomas, CFG, Green, F and Marshall, EJP (1997) Distribution, dispersal and population size of the ground beetles, Pterostichus Melanarius (illiger) and Harpalus rufipes (Degeer) (Coleoptera, Carabidae), in field margin habitats. Biological Agriculture & Horticulture 15, 337–352.CrossRefGoogle Scholar
Thomas, CFG, Parkinson, L and Marshall, EJP (1998) Isolating the components of activity-density for the carabid beetle Pterostichus Melanarius in farmland. Oecologia 116, 103–112.CrossRefGoogle ScholarPubMed
Tuf, IH, Dedek, P and Veselý, M (2012) Does the diurnal activity pattern of carabid beetles depend on season, ground temperature and habitat? Archives of Biological Sciences 64, 721–732.CrossRefGoogle Scholar
Virkar, Y and Clauset, A (2014) Power-law distributions in binned empirical data. The Annals of Applied Statistics 8, 89–119.CrossRefGoogle Scholar
Wehner, R (2001) Polarization vision–a uniform sensory capacity? Journal of Experimental Biology 204, 2589–2596.Google ScholarPubMed
Wallin, H and Ekbom, B (1994) Influence of hunger level and prey densities on movement patterns in three species of Pterostichus Beetles (Coleoptera: arabidae). Environmental Entomology 23, 1171–1181.CrossRefGoogle Scholar
Supplementary material: File

Bailey et al. supplementary material

Bailey et al. supplementary material

Download Bailey et al. supplementary material(File)
File 787.9 KB